Telecom doi: 10.3390/telecom7050127

Authors: Rito

By adjusting antenna parameters, flexible antenna systems can dynamically reconfigure wireless channel characteristics to improve system performance. Non-orthogonal multiple access (NOMA) is a promising multiple access technique for enhancing the spectral efficiency and connectivity of next-generation wireless systems. A key research focus is how to leverage the synergy between NOMA and flexible antenna systems. A novel type of flexible antenna system called pinching-antenna system can provide line-of-sight (LoS) links to users by adjusting the position of the pinching antenna, mitigating large-scale fading. In this paper, we investigate how to implement reliable communication in a downlink NOMA-assisted pinching-antenna system by minimizing the symbol error rate (SER). We derive the SER expressions for NOMA-assisted pinching-antenna system users with QAM modulation, and minimize it with respect to the power allocation factor and the position of the pinching antenna. At higher-order modulation, it is revealed that the minimum SER strategy is for the pinching antenna to track the strong NOMA user. Finally, the analysis is verified by Monte Carlo simulation.

Telecom doi: 10.3390/telecom7050126

Authors: Ling Yang Wenhao Li Liang Tang Zhengyun He Junfeng He Yunhui Liu Zhusong Su Yanzhe Tan

To address the issue that transmitters in existing decode-and-forward (DF) cooperative covert communications hardly acquire all the required channel state information (CSI), this study proceeds as follows. First, an adaptive power strategy for cooperative covert communications based on partial CSI is proposed. Second, the closed-form analytical expression of the covert constraint is derived using the Kullback–Leibler (KL) divergence. Finally, an optimization problem is formulated with the objective of maximizing the covert transmission rate, and the optimal selection of power control parameters is derived. The numerical results demonstrate that in cooperative covert communications, the adaptive power strategy significantly enhances the system’s covert transmission rate compared to the constant power strategy, particularly under relaxed covert constraints. Meanwhile, the proposed strategy also markedly reduces the transmission outage probability. Overall comparative analysis confirms that the adaptive power strategy effectively improves the comprehensive covert performance of the system.

Telecom doi: 10.3390/telecom7050125

Authors: Satoshi Yamakawa Minseok Kim Kenji Matsushita Kensuke Matsui

This paper investigates millimeter-wave cluster characteristics for intra-vehicle access links in a passenger vehicle cabin. Double-directional measurements at 61.5 GHz were conducted for two selected receiver locations near the door switches. High-resolution multipath components were extracted from the measured channel transfer functions and subsequently grouped into clusters. Based on the identified clusters, the intra-cluster delay and angular characteristics were characterized and parameterized for the measured scenarios. The results indicate that the dominant clusters are primarily formed by single reflections with relatively small excess delays, while higher-order reflections contribute only weak power. The resulting scenario-specific cluster parameters provide a physically interpretable basis for evaluating future high-data-rate intra-vehicle wireless links.

Telecom doi: 10.3390/telecom7050124

Authors: Juan Eduardo Macias-Rivera José Ricardo Gómez-Rodríguez Ana Gabriela Castañeda-Miranda Remberto Sandoval-Arechiga Víktor Ivan Rodríguez-Abdala Salvador Ibarra-Delgado

Packet delay variation (jitter) affects the reliability and perceived quality of real-time IP services, but its models and metrics remain fragmented across layers and technologies. This structured survey synthesizes research published from 1990 through August 2026 in three methodological families: stochastic and analytical foundations, simulation and empirical approaches, and cross-layer application-aware methods. The evidence shows a progression from single-queue formulations to end-to-end, heavy-tailed, self-similar, and data-driven models. A reproducible illustration using 1440 RIPE Atlas measurement windows demonstrates why metric choice matters: the 99th percentile of absolute consecutive RTT differences was 8.17 ms, excess-over-minimum RTT reached 52.36 ms, and an RFC 3550-style recursive estimator peaked at only 6.14 ms. The main unresolved problems are unified modeling across heterogeneous wired–wireless paths, comparable high-resolution measurement, cross-layer QoE prediction, reproducibility, and timing-channel security. Priority directions are distribution-aware reporting, open benchmark traces, hybrid analytical–learning models, and stable closed-loop control for 5G/6G, TSN/DetNet, SDN/NFV, and edge networks.

Telecom doi: 10.3390/telecom7050123

Authors: Francis Kagai Philip Branch Jason But Rebecca Allen

Emergency, remote, and infrastructure-constrained environments cannot always rely on cellular networks or continuous Internet connectivity. LoRa offers an alternative for direct, long-range communication between low-power devices without depending on nearby cellular infrastructure, but its low data rate and variable transmission time create new coordination challenges. When multiple LoRa nodes must agree on information, fixed timing can either introduce unnecessary delay or cause missed deadlines as radio conditions change. This paper introduces a cross-layer controller that adapts mesh coordination timing to LoRa time-on-air at runtime. A MAPE-K loop maps estimated airtime into transmission-slot spacing S(c) and deadlines D(c), coupled with quorum or full-participation finalisation and a bounded single-retry mechanism. The framework was implemented on four SX1276 nodes at 915 MHz and evaluated over 2514 coordination rounds under crash and omission faults. Three-of-four quorum coordination achieved a median latency of 2.1 s, compared with 5.3 s for full participation. Overall, 88.6% of rounds met the first adaptive deadline, 11.4% required one retry, and none exhausted the retry budget or violated the evaluated safety invariants. A replay-derived fixed-SF12 schedule produced a median completion time of 11.3 s, compared with 3.4 s for adaptive execution. These results show that LoRa airtime can serve as a practical runtime signal for adaptive coordination on constrained mesh nodes.

Telecom doi: 10.3390/telecom7050122

Authors: Ioannis Christakis Spyridon Mitropoulos Stylianos Katsoulis Odysseas Tsakiridis Dimitrios Rimpas

Digital television is an integral part of modern society, and the quality of the service its has exceeded all expectations. Television stations are divided into national and regional licensing categories, governed by the broadcasting regulations of each European Union member state. DVB-T gap fillers are used to provide and enhance the television signal in rural areas using satellite transport streams (TS) as feeds. However, for regional television stations—particularly in areas lacking network coverage—retransmitting their transport streams via standard digital terrestrial reception and rebroadcasting is often insufficient. This direct Re-transmission method frequently suffers from severe signal intermittency and broadcast interruptions. This paper presents the design, field deployment, and long-term evaluation (8760 h) of an ultra-low-cost, license-exempt DVB-over-IP gap filler architecture. The proposed system integrates Commercial-Off-The-Shelf (COTS) devices to convert a pristine DVB-T transport stream into an IP data stream, transmit it via a 5.64 GHz wireless bridge to bypass natural obstacles, and accurately reconstruct the digital TV signal at the remote gap filler. Empirical results demonstrate that the proposed IP stream method yields a 95.51% reduction in total annual downtime, elevating link availability from 86.26% to 99.38%. The system virtually eliminates environmental signal interruptions, with the only recorded downtime caused by a local power outage. Notably, this robust reliability is achieved at approximately 5% of the CEcapital expenditure (CapEx) required for conventional professional microwave backhaul solutions. Furthermore, the residual bandwidth of the wireless IP backbone provides a ready-made foundation for deploying future municipal network services, such as local Wi-Fi hotspots and LoRa-based Internet of Things (IoT) telemetry networks.

Telecom doi: 10.3390/telecom7050121

Authors: Tahiri Nadia Hafidha Mohammed Brahimi Emad Abd-Elrady Riyadh Bouddou

Beamforming for linear antenna arrays (LAAs) has become a critical research topic in advanced 5G and emerging 6G wireless communication systems due to the increasing demand for high spectral efficiency, interference mitigation, and enhanced radiation performance. This paper proposes an enhanced hybrid optimization framework based on Harris Hawks Optimization integrated with the Covariance Matrix Adaptation Evolution Strategy (HHO–CMA-ES) for unified LAA synthesis. The proposed approach simultaneously optimizes excitation amplitudes, phase shifts, and inter-element positions to achieve substantial peak sidelobe level (PSLL) reduction while preserving desirable beam characteristics. The optimization performance of the proposed method is systematically evaluated against several widely used metaheuristic algorithms, including the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Flower Pollination Algorithm (FPA), and conventional Harris Hawks Optimization (HHO), under identical simulation conditions. Simulation results obtained for a 20-element LAA demonstrate the superior effectiveness of the proposed HHO–CMA-ES framework across multiple optimization scenarios. In amplitude-only optimization, the proposed method achieves a PSLL of −41.746 dB, corresponding to an improvement of approximately 29.3% compared with WOA and more than 88% relative to GA. For amplitude–phase optimization, HHO–CMA-ES improves PSLL by nearly 21% compared with WOA. In the amplitude–position scenario, the proposed approach achieves the best performance with a PSLL of −44.54 dB, yielding an approximately 63% improvement over PSO and more than 111% over GA. Furthermore, the proposed framework exhibits faster convergence, improved solution stability, and enhanced beamforming robustness, confirming its suitability for high-dimensional antenna synthesis problems in future 5G/6G communication systems.

Telecom doi: 10.3390/telecom7050120

Authors: Sooyoung Jang Eunkyung Kim

GPU LDPC latency comparisons across implementations are challenging due to variations in decoder schedules, batch granularity, and timing boundaries. Batch completion was characterized for FP32-layered and flooding decoders on a GPU, considering block acquisition, matched outcomes, and three timing boundaries. The schedule study included 600,000 observations. Flooding exhibited higher P50 and P99 in 15 full-boundary matched-cap comparisons, while J99 and R99 did not display uniform schedule ordering. For words satisfying the syndrome under both schedules, layered decoding achieved first satisfaction earlier in every estimable 2- and 4-dB cell. Layered endpoint levels varied among full, decode-only, and transfer-only boundaries. Over six sessions, full-boundary J99 increased by 234.7 μs from batch 64 to 128 (pointwise descriptive 95% percentile interval, 196.1–281.5 μs), which contrasted with two other designs. These results pertain to a single dynamically clocked Windows Subsystem for Linux 2 (WSL2) GPU and do not establish fixed-clock or cross-platform behavior, radio-interface latency, causal attribution, or worst-case bounds.

Telecom doi: 10.3390/telecom7050119

Authors: Vladimir Volman

Non-cooperative sensing using signals of opportunity traditionally requires an explicit reference signal for target detection and localization. This paper introduces a reference-free sensing framework in which target geometry is inferred directly from the received waveform rather than by comparison with an acquired or reconstructed illuminator signal. The proposed framework is implemented using the Ranging, Detection, Imaging, Communications, Approach, and Landing (RaDICAL) architecture, which combines a hybrid Dish–Sparse Uniform Circular Array (SUCA) receiver with Starlink downlink transmissions as spaceborne illuminators of opportunity. Deterministic Multifrequency Dither (DMD) applied across the SUCA elements transforms spatial diversity into unique composite waveform signatures. A unified electromagnetic and signal-processing model is developed that combines spherical-wave propagation, parabolic focusing, deterministic multifrequency modulation, and QR-based waveform-domain hypothesis testing for direct target localization. Numerical simulations together with link-budget analysis demonstrate the feasibility of the proposed approach. Single-dwell detection of 0 dBsm targets is achieved at physical signal-to-noise ratios near 0 dB, while near-unity detection probability is obtained above 10 dB SNR under controlled false-alarm conditions. The results demonstrate that commercial Starlink LEO communication satellites can serve as practical illuminators of opportunity for reference-free non-cooperative sensing without requiring acquisition or reconstruction of the transmitted illuminator waveform.

Telecom doi: 10.3390/telecom7050118

Authors: Ghaffer Iqbal Kiani

This work introduces a novel self-sustainable wireless communication architecture that combines RIS and NOMA with a hybrid acoustic–magnetic energy harvesting framework. The source node operates under strict energy constraints and is powered by harvesting ambient acoustic vibrations and surrounding magnetic fields, enabling autonomous transmission to multiple NOMA users. To enhance propagation conditions, an RIS is deployed to intelligently control the wireless channel by optimizing its phase response, thereby strengthening desired signals and suppressing interference.The performance of the proposed system is analytically characterized under the hybrid energy harvesting model. The derived expressions provide insight into the interaction between harvested energy dynamics, RIS configuration, and NOMA transmission. Simulation results confirm that the proposed RIS-NOMA architecture significantly outperforms conventional orthogonal and non-orthogonal access schemes in both spectral and energy efficiency. In addition, the hybrid harvesting mechanism ensures more stable energy availability, improving reliability in highly energy-constrained environments.

Telecom doi: 10.3390/telecom7050117

Authors: Weicheng Qiu Jiujiu Chen Bangshu Xiong Wei Li

In resource-constrained unmanned aerial vehicle (UAV) infrared image transmission, infrared small targets usually occupy only a small number of pixels, while conventional uniform resource allocation strategies fail to consider the semantic differences among image regions, resulting in inefficient resource utilization and loss of target-related information. To address this issue, this paper proposes a semantic-aware resource allocation method for infrared small-target detection. First, infrared images are divided into multiple grid cells, and the semantic importance of each cell is estimated based on preliminary detection results. Then, the cells are classified into different semantic levels, followed by differentiated bit allocation and region-wise reconstruction. Finally, a joint optimization problem of grid partitioning and resource allocation parameters is formulated and solved by the covariance matrix adaptation evolution strategy (CMA-ES) to optimize both detection performance and image reconstruction quality. Experimental results demonstrate that the proposed method achieves mIoU values of 0.6106 and 0.7445 at available bit rates of 12.5% and 50%, respectively, showing significant improvements over comparison methods. Moreover, CMA-ES obtains stable optimization results with fewer evaluations and provides a favorable balance between detection accuracy and reconstruction quality. These results indicate that the proposed method can effectively preserve target-related semantic information under limited communication resources, providing an effective solution for efficient UAV infrared image transmission and reliable small-target detection.

Telecom doi: 10.3390/telecom7050116

Authors: Ghaffer Iqbal Kiani

This paper proposes a novel framework integrating Holographic Intelligent Surfaces (HISs) with magnetic energy harvesting at the source node to enable energy-efficient wireless communication. In the proposed system, energy harvesting is performed at the transmitter using magnetic field coupling, eliminating reliance on conventional power supplies. The harvested energy is then used to generate and transmit signals, which are intelligently manipulated by the HIS to enhance propagation conditions between the source and the destination. By leveraging the continuous electromagnetic control capability of HIS, the system improves signal strength, coverage, and reliability. Analytical insights highlight the potential of combining magnetic energy harvesting with HIS to support sustainable and self-powered wireless networks, particularly for next-generation communication systems.

Telecom doi: 10.3390/telecom7050115

Authors: Viktor Stoynov Dimitriya Mihaylova

This paper presents a multi-dimensional robustness evaluation framework for ultra-dense Internet of Things (UD-IoT) networks in smart city environments. The framework addresses the need to assess robustness beyond isolated indicators such as latency, throughput, packet loss, or availability by integrating operational continuity (OC), scalable resource efficiency (SRE), network flexibility (NF), and security (SEC) into a unified robustness metric (R). Building on an earlier formulation of the metric, the present study extends its interpretation toward smart city IoT deployments, strengthens the perception layer perspective, and introduces a controlled fifth-generation (5G) connectivity validation procedure. The evaluation is performed in two complementary stages. First, simulation-based analysis is conducted across critical, essential, and basic deployment scenarios to examine the mathematical behavior of the metric under different operational priorities and parameter ranges. Second, a validation setup based on free5GC and UERANSIM is used to transform observable control-plane, user-plane, service-connectivity, and security indicators into comparable robustness components through an explicit normalization and mapping procedure. The results show scenario-dependent robustness degradation across the evaluated smart city IoT conditions, with the strongest degradation in the combined stress scenario where endpoint density, traffic intensity, service recovery stress, and abnormal access conditions occur simultaneously.

Telecom doi: 10.3390/telecom7050114

Authors: Janibek F. Kurbanov Abdulaziz T. Botirov Begali Turdialiyev Aziz Saitov Rashid Nasimov

In most railway divisions the results of scheduled inspections of automation and telemechanics field devices are still recorded on paper. Such records reach engineering management with a delay, are easy to lose, and are difficult to verify. This paper examines the data transmission core of a digital inspection complex in which passive RFID tags identify both the equipment and the personnel, and the inspection record is delivered to a cloud server over a hybrid cellular architecture combining a failure-prone GSM channel with a reliable CDMA channel. To quantify the stability of such a system, a continuous-time Markov chain model is constructed in which the link is represented as an M/M/2/K queue with one unreliable server: both channels carry traffic in parallel, and during a GSM outage the CDMA channel alone sustains service. Records already admitted are preserved across a channel switch; only records arriving at a full shared buffer are rejected. This residual overflow loss stays below 0.1% at routine load with a buffer of m ≥ 5 and reaches about 2.8% only under post-incident overload. The model parameters were measured on an operating ESP32-based scanner complex piloted at Hamza station on 46 point machines. Calculations for three load scenarios show that increasing the local buffer beyond m = 5 yields diminishing returns while the delay grows, and that resilience is governed primarily by the presence of the redundant channel and adequate buffering, with the primary-channel recovery rate a secondary factor.

Telecom doi: 10.3390/telecom7050113

Authors: Guy E. Toibin Yotam Lurie Shlomo Mark

Telecommunications infrastructures are increasingly cloud-native, multi-vendor, and mission-critical environments, integrating 5G core networks, virtualized network functions, and software-defined infrastructure that expand the operational attack surface and make robust security architecture a core engineering requirement. Zero-Trust Architecture (ZTA) has emerged as the leading technical paradigm for securing these environments through continuous authentication and policy-based access control; however, technical Zero-Trust controls alone do not guarantee successful deployment, and large-scale deployment introduces significant socio-technical and governance challenges that existing engineering-focused frameworks only partially address. This study makes two contributions: it provides longitudinal evidence on the impact of ZTA on organizational trust using an extended Technology Acceptance Model (TAM) that incorporates Perceived Trust, and it proposes a Proactive Trust Management Playbook (PTMP) for telecommunications infrastructure organizations that complements technical Zero-Trust deployments through organizational governance. The study draws on a five-wave repeated cross-sectional longitudinal case study conducted between 2020 and 2023 in a multinational telecommunications infrastructure organization. The five waves span three organizational phases, enabling an assessment of employee perceptions of usefulness, ease of use, and trust before and after ZTA deployment and following a structured governance intervention. The findings reveal a substantial decline in the composite TAM index following ZTA implementation (−24%, Cohen’s d = 1.12), with no meaningful spontaneous recovery over time (d = 0.08). A structured Communication Campaign was associated with a partial but incomplete recovery (d approximately 0.47), indicating that trust erosion under ZTA is measurable and suggesting that trust recovery is shaped more by governance interventions than by technological adaptation alone. The proposed PTMP complements technical Zero-Trust architectures by strengthening organizational trust and governance in telecommunications infrastructure environments.

Telecom doi: 10.3390/telecom7050112

Authors: Yahya Saeed Lway Abdulrazak

Network slicing allows a single 5G New Radio (NR) carrier to serve services with significantly different reliability targets. However, the cost of each slice in spectral and energy terms, once it is realized as a concrete uplink, is rarely measured. This paper reports a link-level evaluation of the joint energy efficiency (EE) and spectral efficiency (SE) trade-off in a multi-slice 5G-NR uplink in which enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and massive Machine-Type Communication (mMTC) each receive a distinct Physical Uplink Shared Channel (PUSCH) configuration matched to a block error rate (BLER) target of 10−3, 10−5, and 10−1, respectively. Using a 3GPP-compliant simulator built on the MATLAB 5G Toolbox (R2026a), 1440 operating points across different frequency bands, FR1 and FR2, propagation delay profiles, transmit power levels, and user distances from the gNB were evaluated. Each point is evaluated twice, with and without a co-channel fixed-service (FS) interferer. Every slice exhibits an interior energy-optimal transmit power; the three slices occupy clearly separated regions of the EE–SE plane, ordered eMBB above mMTC above URLLC. Finally, FS interference reshapes the trade-off through one mechanism with very different consequences per slice: eMBB, which earns its rate from 256-QAM, loses approximately a quarter of its peak throughput and roughly half of its peak EE under line-of-sight (LoS), while mMTC is the most resilient and URLLC shows the sharpest qualitative change, with its energy optimum migrating by approximately 16 dB. Therefore, the energy-optimal operating point is not a fixed property of the band, distance, and slice, but a function of the interference that the link actually sees; thus, slice-aware uplink power control must also be interference-aware.

Telecom doi: 10.3390/telecom7050111

Authors: Ling He Qingrui Guo Xuerang Guo Huiting Yang Yanan Xin

Distributed multiple-input multiple-output (MIMO) is a promising architecture for future wireless systems because cooperation among geographically separated base stations (BSs) improves coverage, spectral efficiency, and link reliability. However, the large effective aperture formed by distributed BSs makes near-field effects non-negligible and complicates accurate channel state information acquisition. Existing near-field estimators often suffer from modeling errors caused by approximate angle–range decoupling or from the high storage and computational costs of dense two-dimensional sparse representations. This article proposes an off-grid variational Bayesian channel-estimation framework for the considered distributed near-field MIMO geometry, which comprises equally spaced, collinear BS reference points and aligned uniform linear arrays (ULAs) with common inter-element spacing. We establish a geometry-coupled model based on the exact geometric spherical-wave phase response and map the local direction–range parameters observed by different BSs into a common reference coordinate system, yielding a two-dimensional jointly sparse representation. An independent-vector variational Bayesian inference algorithm then decomposes the high-dimensional multiuser recovery problem into user-specific posterior subproblems. It operates directly on the received pilot matrices, avoiding pilot–matrix inversion and the resulting distortion of noise statistics. A two-dimensional skewed off-grid update is further embedded in an expectation-maximization procedure to jointly refine angle and range offsets, mitigating basis mismatch while permitting a coarser initial dictionary. Simulation results support the effectiveness of the proposed method in the evaluated scenarios.

Telecom doi: 10.3390/telecom7050110

Authors: Taoreed Akinola Xiangfang Li Lijun Qian

Reliable dynamic spectrum access in wide-area cognitive radio networks (CRNs) is challenged by sparse and erroneous spectrum-sensing measurements. This work formulates spatial-spectrum occupancy reconstruction as an image inpainting problem and proposes two cascaded deep learning models: a bidirectional long short-term memory-based image inpainting model (BiLSTM-IIM) and a binary diffusion-based image inpainting model (Diff-IIM). In both models, Stage 1 corrects sensing errors at observed locations, while Stage 2 reconstructs missing entries. The models are evaluated using simulations of a 2500m×2500m cognitive radio network with 50–200 secondary users, five primary users, and three spatial resolutions under representative wireless conditions. Both models generally outperform total variation and matrix completion baselines under sparse and noisy observations. Under moderate sensing errors, BiLSTM-IIM achieves accuracies of 92.91%, 93.66%, and 91.17% at the 10×10, 20×20, and 30×30 resolutions, respectively, while Diff-IIM achieves lower false-alarm rates with fewer parameters. Stage 1 reduces the sensing-error rate by approximately 58% for BiLSTM-IIM and 61% for Diff-IIM. These results support deep learning-based inpainting for wide-area spectrum occupancy reconstruction under the evaluated conditions.

Telecom doi: 10.3390/telecom7050109

Authors: Supun Fernando Uditha Wijewardhana Nishan Dharmaweera

We propose optimal power allocation for a transmitter that operates with either the ARQ-I or CC-HARQ protocol in an interference-limited environment where both the desired and interfering signals experience Rayleigh fading, forming an analytically tractable non-line-of-sight scenario, to achieve ultra-reliable communication with minimal power. We formulate the power allocation problem as a minimization of the average retransmit power under a given target outage probability for both retransmission schemes. Using the Karush–Kuhn–Tucker method, we derive protocol-specific equations solved numerically for retransmit powers. Unlike prior interference-limited numerical studies and noise-limited closed-form results, we derive closed-form analytical solutions for optimal retransmit power expressions for ARQ-I and CC-HARQ in a Rayleigh–Rayleigh interference-limited environment for two retransmissions, which act as computationally efficient benchmarks for real-time ultra-reliable applications and establish a baseline for extending to more general fading environments. This constitutes the primary contribution of this work. We show through simulations that the proposed schemes achieve significant power savings compared to conventional open-loop transmission, particularly in the ultra-reliable region. Benchmarking against prior Rician–Rayleigh results shows that, as expected, the Rayleigh–Rayleigh case requires higher power because it lacks the line-of-sight component that improves reliability, while confirming that CC-HARQ consistently outperforms ARQ-I in power efficiency.

Telecom doi: 10.3390/telecom7050108

Authors: Leonel R. Perea-Trejo Roilhi F. Ibarra-Hernández Francisco R. Castillo-Soria José A. Del-Puerto-Flores Jesus Acosta-Elias Abel Garcia-Barrientos Bersain A. Reyes

Deep learning (DL) has emerged as a promising alternative for signal detection in multiple-input multiple-output (MIMO) wireless communication systems, achieving a performance close to the optimal maximum likelihood (ML) criterion. However, implementing DL algorithms for MIMO signal detection still demands a high computational cost. Recently, a DL-based architecture with reduced complexity was proposed for MIMO signal detection. More specifically, a preprocessing stage was added to label the input signals, resulting in a more efficient detection scheme. However, even with this reduction in complexity, the proposed detection schemes remain computationally demanding, and this cost grows sharply as the system scales to higher-order modulations and/or large antenna arrays. To further reduce the complexity of these schemes, this paper proposes a graphics processing unit (GPU)-accelerated procedure for DL-based MIMO signal detection. The algorithm is accelerated by using two strategies in a CUDA-enabled PyTorch implementation. First, we optimize the training, which tightens the bit error rate (BER) gap from 1.00 dB to approximately 0.20 dB for the One-Hot (OH) labeling and from 2.00 dB to approximately 0.90 dB for the One-Hot-per-Antenna (OHA) labeling. Additionally, we optimize the Monte Carlo simulation using seven strategies, reducing the end-to-end evaluation time by 34.7% on an NVIDIA RTX 4090 GPU. These results aim to support future projects in evaluating DL-based MIMO systems using shorter computing windows.

Telecom doi: 10.3390/telecom7050107

Authors: Laurence Banda Etienne Alain Feukeu

The digital divide in developing economies persists as a multidimensional challenge encompassing infrastructure access, digital skills, usage patterns, and social inequality. This paper presents a structured literature review of 63 peer-reviewed articles (2018–2025) examining how intelligent connectivity, the convergence of fifth-generation (5G) mobile networks, artificial intelligence (AI), and the Internet of Things (IoT), can contribute to bridging this divide. The findings reveal that intelligent connectivity offers transformative potential across agriculture, healthcare, education, and financial services. However, its impact is contingent upon enabling governance, institutional capacity, and digital skills. Three contributions emerge: (1) a conceptual framework specifying directional pathways from enabling conditions to intelligent connectivity deployment and inclusive outcomes; (2) a comparative regional analysis (Sub-Saharan Africa, Southeast Asia, and Latin America) identifying context-specific barriers and opportunities; and (3) a socio-technical model positioning intelligent connectivity as an integrated system rather than a purely technological solution. A key limitation is acknowledged: only 16% of the reviewed corpus addresses developing economies, necessitating triangulation with institutional reports from the International Telecommunication Union (ITU), Organization for Economic Co-operation and Development (OECD), and Global System for Mobile Communications Association (GSMA). The paper concludes with open research challenges and policy recommendations for inclusive digital transformation.

Telecom doi: 10.3390/telecom7050106

Authors: Sirine Hamrouni Jean-Yves Baudais Stéphane Méric Adnen Cherif

Orthogonal time-frequency space (OTFS) modulation is a promising waveform for integrated sensing and communication (ISAC) in high-mobility environments, where both large Doppler shifts and target delays must be accurately handled. Existing CP-OTFS radar analyses often assume CP-preserving target delays, for which the received echo remains inside the protected interval and CP-induced interference is avoided. However, practical sensing scenarios may involve large Doppler shifts and large target delays exceeding the cyclic-prefix duration, leading to CP violation and interference. This paper extends the analytical performance evaluation of cyclic-prefix OTFS (CP-OTFS) to this large-delay regime while also accounting for Doppler shifts. Starting from the CP-OTFS transmit signal, point-target channel, time-frequency demodulation, and delay-Doppler matched filtering, closed-form expressions are derived for the radar matched-filter output statistics, including the mean response, average delay-Doppler energy, main-lobe energy, peak sidelobe level ratio (PSLR), and integrated sidelobe level ratio (ISLR). For the communication function, an analytical error vector magnitude (EVM) expression is derived to quantify the degradation induced by large delays. The analytical expressions are evaluated as functions of target delay and Doppler shift and validated through end-to-end CP-OTFS ISAC simulations. The results demonstrate the accuracy of the analytical expressions and quantify the reduction in useful energy, the increase in interference, the degradation of the PSLR and ISLR, and the increase in EVM.

Telecom doi: 10.3390/telecom7040105

Authors: Cem Gocen

Compact sub-6 GHz and wireless local area network (WLAN) multiple-input multiple-output (MIMO) antennas require broad impedance coverage and low inter-port coupling within limited footprints. This work presents a two-port Koch-fractal U-slot antenna with a defected ground structure (DGS) on RT/duroid 5880. The design evolves from a rectangular monopole through Koch-edge shaping, U-slot loading, and ground-plane defects. The fabricated two-port prototype exhibits a measured −10 dB impedance bandwidth of 3.07–6.02 GHz, covering n78, n79, and WLAN, while the measured inter-port isolation exceeds 18.13 dB. The fabricated single-element prototype provides measured realized gains of 1.92, 2.34, and 2.05 dBi at 3.5, 4.7, and 5.5 GHz, respectively. Measurement-derived MIMO metrics yield an envelope correlation coefficient not exceeding 0.002, diversity gain close to 10 dB, channel capacity loss of 0.07–0.10 bits/s/Hz, mean effective gain near −3.1 dB with zero port imbalance, and acceptable in-phase total active reflection coefficient behavior. WLAN-band quadrature phase-shift keying tests at 5.18, 5.50, and 5.825 GHz produce error vector magnitude values of 5.4–9.1%, with derived bit error rate estimates below 10−6 under an additive white Gaussian noise assumption. The design provides wide measured bandwidth, good isolation, low correlation, and WLAN-band signal-domain validation in a simple printed structure.

Telecom doi: 10.3390/telecom7040104

Authors: Xingyu Tian Citong Que Faisal Nadeem Khan

Federated learning (FL) enables cellular traffic prediction without centralizing raw base-station data, but statistical heterogeneity makes a single global model unsuitable for many clients. This paper proposes federated clustering with adaptive personalization (FedCAP), a parameter-efficient personalized FL framework that separates cluster-level representation learning from client-level adaptation. Clients are grouped using training-only daily traffic profiles, after which an LSTM backbone is trained by FedAvg within each cluster. Each client then freezes the cluster backbone and optimizes a residual bottleneck adapter locally. The adapter contains 4241 trainable parameters, 6.19% of the 68,483-parameter three-feature backbone and prediction head, and personalization transmits no model updates. In a shared-seed-42 comparison across 11 methods and four public datasets, FedCAP ranks first or second in 12 of 16 dataset–metric combinations. Across five shared seeds, its mean MAE is 6.66%, 7.47%, 2.64%, and 3.86% below FedAvg on the Milan, Trentino, Bihar, and Taiwan datasets, respectively. Holm-adjusted paired t-tests identify 6 significant dataset–metric differences, whereas exact Wilcoxon tests are not significant because each comparison contains only five nonzero seed-matched pairs; the statistical evidence is therefore interpreted conservatively.

Telecom doi: 10.3390/telecom7040103

Authors: Nixon Mateo Herrera Astudillo Luigi O. Freire Luis Navarrete Gabriel Inca Yajamín

Industrial supervision can be expanded through the Internet of Things (IoT) without moving the control logic outside the PLC. This study evaluates a level-monitoring and control architecture using a point-to-point LoRa link between two Siemens S7-1200 PLCs; LoRaWAN is used solely as a conceptual architectural reference, and no gateway, network server, or OTAA/ABP procedures were implemented. An Arduino Uno with an Ethernet Shield W5100 exchanges variables with the PLC through Modbus TCP and transfers them via UART to Heltec LoRa ESP32 modules. Factory I/O simulates the process, and Adafruit IO provides remote supervision. The field campaign covered twelve locations between 10 and 120 m and 1200 frames. Reception, packet loss, RSSI, SNR, and the latency value calculated by the firmware were recorded. Overall reception was 94.17%, packet loss was 5.83%, and the mean latency value was 727.17 ms. The main contribution is the separation of local control from wireless communication and the quantitative evaluation of the link. Because the PLC maintained control when frames were lost, the solution is suitable for supervising slow processes, but not for critical loops. The results are specific to the evaluated radio and firmware configuration.

Telecom doi: 10.3390/telecom7040102

Authors: Hulin Li Conglu Huang Zhongyu Yang Yitao Li

Low Earth orbit mobile satellite system (LEO-MSS) is a major system that provides communication support for mobile terminals beyond the coverage of terrestrial communication systems. However, passive handover happens frequently, caused by the quick movement of LEO satellites, making it hard to guarantee quality of service (QoS) for handover users while maintaining a large number of users. To tackle this problem, we propose a novel soft handover scheme and combine it with uplink power-domain non-orthogonal multiple access (PD-NOMA) for the first time to guarantee QoS for handover users and improve uplink throughput. We analyze the uplink PD-NOMA-based soft handover scheme with three users in two beams and give the closed-form expression of the optimal uplink transmission power allocation. Afterward, we introduce this method into a practical multi-beam LEO-MSS system with multiple users and sub-channels and formulate the optimization problems to maximize system throughput. Numerical results show that the proposed uplink PD-NOMA-based soft handover scheme provides much better performance on throughput and fairness for heavy loads.

Telecom doi: 10.3390/telecom7040101

Authors: Juan C. Navarro-Ramos Duvan Darío Quintero-Cardozo María E. Rojas-Méndez Nelson A. Pérez-García Ángel D. Pinto-Mangones Juan M. Torres-Tovio Octavio A. Torres-Medina Yair Rivera Julio

This work presents, for the first time, visibility measurements carried out in five locations in Colombia (Yopal, San Andrés, Montería, Puerto Carreño, and Villavicencio) during their respective months of lowest visibility, using Vaisala FD70 forward-scatter sensors with a 1-min integration time. The cumulative distributions (CDs) of visibility obtained are fundamental for estimating fog attenuation in free-space optical (FSO) communication systems operating at optical frequencies. Additionally, the performance of visibility prediction models reported in the literature (the Sousa, Queluz, and Rodrigues model, hereafter referred to as the SQR model, and the Pinto I and Pinto II models) is evaluated within the Colombian climatic context. It was observed that the Pinto I and Pinto II models exhibited the best overall performance among the existing models, with a root mean square error (RMSE) value close to 11.46 km for the set of locations, while the locality of San Andrés presented the most adverse conditions, with visibilities below 50 m for 0.01% of the time during the critical month of November. A link-budget analysis shows that the use of visibility prediction models can lead to severe underdimensioning or overdimensioning of FSO links, highlighting the need for locally measured visibility data.

Telecom doi: 10.3390/telecom7040100

Authors: Evangelos D. Spyrou Vassilios Kappatos Constantinos T. Angelis Chrysostomos Stylios

The integration of low-Earth-orbit (LEO) satellites with unmanned aerial vehicles (UAVs) promises high-throughput and flexible wireless connectivity, yet it faces critical challenges in simultaneously guaranteeing data rates and long-term energy harvesting under mobility and imperfect channel state information (CSI). Additionally, the rate–energy trade-off imposed by simultaneous wireless information and power transfer (SWIPT) further complicates per-slot resource allocation. In this paper, we propose a Lyapunov-based scheduling framework that stabilizes UAV data and virtual energy queues while maximizing weighted throughput. The framework employs a custom inner solver combining successive convex approximation (SCA) and weighted minimum mean-square error (WMMSE) optimization to efficiently compute per-slot beamformers and power-splitting ratios. Our approach explicitly accounts for UAV mobility, Rician fading channels with Doppler, and circuit nonlinearities in energy harvesting, ensuring feasible and energy-aware SWIPT operation. A LEO satellite–UAV integrated communication system is considered, where multiple satellites provide wireless connectivity to energy-constrained UAVs operating in a dynamic three-dimensional environment. The satellites employ multi-antenna transmission, while the UAVs rely on energy harvesting mechanisms to sustain their operation. The communication links are characterized by dominant line-of-sight propagation conditions, and UAV trajectories are adaptively optimized to improve network performance and energy efficiency. Simulation results demonstrate that the proposed Lyapunov-based SCA-WMMSE framework significantly outperforms a fixed baseline approach, providing substantial improvements in signal quality, achievable data rates, and harvested energy. Moreover, the proposed method maintains stable energy management behavior and guarantees long-term energy sustainability for the UAVs.

Telecom doi: 10.3390/telecom7040099

Authors: Dilmurod Davronbekov Nurmukhamed Shaudenbaev Muhammad Sadiq Cheng Wen Hua Zheng Kuanishbay Sadatdiynov

The expansion of Internet-of-Things (IoT) deployments in 5G New Radio (NR) networks has made periodic measurement reporting a growing burden for low-mobility devices, which benefit little from frequent updates yet must report as often as highly mobile ones. At present, User Equipment (UE) transmits MeasurementReport messages at a fixed ReportInterval—typically 240 ms—regardless of mobility. This continuous transmission needlessly depletes UE battery energy and consumes critical uplink signaling capacity. This paper proposes a five-level adaptive ReportInterval selection scheme driven by the statistical properties of Reference Signal Received Power (RSRP) and Signal-to-Interference-plus-Noise Ratio (SINR). A low-mobility criterion combines four statistical conditions—the variance and gradient of both RSRP and SINR—through a logical AND, while a two-stage hysteresis mechanism (a 3 dB margin and a 2 s holding timer) suppresses unnecessary level transitions. The scheme is slice-agnostic: By relying on observed signal statistics rather than network-slice labels, it serves low-mobility mMTC and stationary eMBB devices while leaving URLLC and high-mobility UEs at their standard configuration. In Monte Carlo simulations over the 3GPP TR 38.901 Urban Micro (UMi) channel model (200 UEs, 300 s, 100 iterations), the algorithm attains a classification accuracy of 91.32% and a sensitivity of 98.77%. Based on the DRX energy model, it yields an estimated 10.87% reduction in average UE power (from 28.15 to 25.09 mW) together with a 51.09% reduction in the network-wide MeasurementReport count. The hysteresis mechanism cuts level transitions by a factor of 31.33 (from 6852.7 to 218.7 per iteration), substantially lowering RRC reconfiguration signaling. Operating at O(n) complexity on the gNodeB and using only conventional MeasConfig signaling, the scheme requires no protocol additions or UE-side modifications, making it directly deployable on existing 3GPP Release 17 infrastructure as a gNB-side software update.

Telecom doi: 10.3390/telecom7040098

Authors: Chenglin Cai Bozhi Wan Kun Xie

Aiming at the problem of high-precision positioning difficulties caused by the complete loss of lock of GNSS signals, strong multipath, and non-line-of-sight (NLOS) propagation in indoor pseudolite positioning, this paper builds a pseudolite positioning prototype system for small-scale indoor scenarios and proposes a Northern Goshawk Optimization-based ambiguity function method (AFM) single-epoch resolution algorithm (AFM-NGO). First, this method constructs the ambiguity function using double-difference carrier phase observations, takes the 3D coordinates of the station as the search variable, and jointly estimates the coordinates and integer ambiguities in the coordinate domain. Then, the Northern Goshawk swarm intelligence optimization algorithm is introduced to perform global and local collaborative search on the AFM search space, avoiding the large computational load of traditional grid search. Meanwhile, the statistical characteristics of multipath residuals and observation noise are explicitly considered in the fitness function, thereby enhancing the robustness of the algorithm in complex indoor environments. Based on a 6 m × 5 m × 2 m indoor strong multipath experimental scenario, 2D and 3D positioning tests were carried out on the pseudolite system. The results show that the proposed AFM-NGO algorithm can achieve stable centimeter-level positioning accuracy through single-frequency single-epoch carrier phase observations without initialization using high-precision known points; the error curve of consecutive epochs is smooth with no obvious outliers. Compared with traditional pseudolite positioning methods, it has smaller 3D root mean square error (RMSE) and better temporal stability of errors, which verifies the effectiveness and engineering application prospects of the algorithm in indoor strong multipath pseudolite positioning applications.

Telecom doi: 10.3390/telecom7040097

Authors: Kanika Duggal Gi-Chon Park

Smart cities (SCs) use the Internet of Things (IoT) to collect and process data to communicate with their infrastructure and assets in real time. A great deal of techniques, such as encryption protocols, Random Forest-based AI-driven threat detection, and blockchain architectures, have been developed to address cybersecurity challenges in smart cities (SCs). These techniques, however, have limitations such as their scalability, high computational expenses, and energy inefficiency. Therefore, in this study, to overcome these challenges, we propose a blockchain-based infrastructure called BlockSafeNet. This uses artificial intelligence, big data, and blockchain to enhance cybersecurity in SCs. The effectiveness of the proposed BlockSafeNet framework was evaluated using responsiveness, computational time, encryption quality score, detection rate, false positive rate, latency, throughput, and energy consumption as the primary cybersecurity performance metrics. These metrics were selected to assess communication efficiency, threat detection capability, privacy preservation, scalability, and overall security performance within smart-city IoT environments. To ensure secure data transactions, robust threat detection, and efficient communication. The system’s high calculation speed and detection rate show potential for managing sensitive maternal health data collected by IoT devices. The platform also shows how IoT may be used by healthcare services to monitor public health in real time, allowing hospitals, emergency services, and public health agencies to securely share data. This aids in resource optimization, improving service delivery, and preserving data privacy and trust in SCs. Data was obtained from the UCI Machine Learning Repository on Kaggle to validate the developed framework. By evaluating the effectiveness of BlockSafeNet in tackling cybersecurity challenges, we establish its practical relevance and usability in SCs. The proposed BlockSafeNet framework achieved a responsiveness of 24 s, an encryption quality score of 0.89, computational time of 85 s, and a detection rate of 91%, demonstrating significant improvements in secure IoT communication, privacy preservation, and AI-driven cyber threat detection within smart city infrastructures. shows that SC IoT security has significantly improved through the adoption of new data protection methods and better measures of security, providing a positive impact on the SC ecosystem.

Telecom doi: 10.3390/telecom7040096

Authors: Oluwole John Famoriji Michael O. Omojoyegbe Ebenezer Esenogho

Reliable estimation of radio wave propagation across irregular terrain is essential for the effective planning and optimization of contemporary wireless communication systems, such as cellular networks, broadcasting infrastructure, and radar systems. The intricate interaction between electromagnetic waves and environmental features—including mountains, depressions, and artificial structures—requires sophisticated modeling approaches capable of accounting for diffraction, reflection, scattering, and shadowing effects caused by terrain variations. The use of the split-step parabolic equation (SSPE) method for modeling radio wave propagation over irregular terrain has become increasingly popular. However, high computational cost limits its practical deployment. This has led to growing interest in machine learning (ML) as a more efficient alternative. A major challenge of ML in electromagnetic applications lies in accurately predicting results for scenarios not represented in the training data—a limitation not yet fully addressed by existing ML-based propagation models. To overcome this challenge, a high-fidelity, scalable physics-inspired modeling framework is presented. The proposed method effectively adapts to various terrain profiles and antenna setups, demonstrating strong extrapolation performance beyond the training set. Furthermore, another key innovation is the integration of prior knowledge from deterministic physics-based models into the neural network architecture. Additionally, tailoring the network structure to reflect the physical characteristics of terrain-based wave propagation significantly enhances both prediction accuracy and scalability.

Telecom doi: 10.3390/telecom7040095

Authors: Zihao Li Jiarong Lin Zexin Lin Lixiang Zuo Mingjia Wang Liyun Zuo

To ensure the guidance accuracy and flight safety of instrument landing systems (ILSs), it is imperative to mitigate multipath interference caused by reflections from airport structures, whose core detrimental effect is the excess deviation of the difference in depth of modulation (DDM). This paper presents an automated design methodology with the explicit objective of directly minimizing DDM, employing a genetic algorithm (GA) to optimize additional metallic baffles adjacent to a building, thereby achieving a “stealth” effect for the building structure. The method encodes the layout parameters of additional metallic baffles adjacent to a building into chromosomes, searching for the optimal configuration through iterative evolution. Each generation applies the efficient half-space multilevel fast multipole method (HSMLFMM)—for the first time in ILS interference simulation—to accurately compute the radiation field of every candidate design. The maximum resultant DDM along the glide path serves as the fitness function for selection. Optimization and validation are conducted for four typical scenarios where the interference source is located 50, 100, 150, and 200 m from the runway centerline. The optimized DDM values are reduced to 4.49, 4.78, 4.63, and 4.87, respectively, all below the ICAO Annex 10 tolerance limit of 5μA for CAT III operations. The corresponding reduction percentages are 75.85%, 83.93%, 90.73%, and 90.19%, with a maximum reduction of 90.73% achieved in the 150 m scenario. This research establishes an efficient, automated closed-loop optimization workflow, which offers a viable approach for the intelligent and precision design of low-observable buildings at airports.

Telecom doi: 10.3390/telecom7040094

Authors: Akhmet Tussupov Yedil Nurakhov Danil Lebedev Madi Shayakhmetov Leila Rzayeva Ulykbek Shambulov Ibraheem Shayea

(1) Background: The disaggregated and software-defined nature of fifth-generation (5G) core networks and the Open Radio Access Network (O-RAN) architecture increase the attack surface and produce large volumes of heterogeneous evidence that must be analyzed in real time to support incident reconstruction. Open-source 5G stacks (including Open5GS and srsRAN) have become reference platforms in the literature, yet recent research, such as the RANsacked study that reported 119 vulnerabilities and 97 unique CVEs across multiple LTE/5G implementations, have highlighted the pressing need for AI-based detection and forensic capabilities specific to these stacks. (2) Methods: We introduce an experimental framework consisting of a reproducible srsRAN+Open5GS testbed and an AI-driven forensic and detection pipeline. The pipeline receives control-plane (NAS, NGAP, F1AP) and Service-Based Interface (SBI) traffic, extracts protocol- and statistically grounded features and classifies traffic into seven attack types using a hybrid CNN–LSTM model. Integrity-protected and timeline-correlated forensic artifacts (PCAP, logs, memory dumps) assist in reconstructing an incident. (3) Results: The proposed hybrid model achieves a macro F1-score of 0.972 and an AUC-ROC of 0.995 (5-fold CV) and degrades gracefully under load. (4) Conclusions: We show that AI-based detection can be coupled with a scientifically sound evidence chain in open-source 5G stacks deployed as disaggregated mobile networks.

Telecom doi: 10.3390/telecom7040093

Authors: Eleftherios G. Tsampasis Christos Pergamalis Mario Sulokoka Orfeas Zervas Charalampos N. Ilias Panagiotis K. Gkonis

The goal of the study presented in this article is to investigate all current issues related to the proper deployment of load forecasting (LF) techniques in smart grids (SGs). The latter concept has recently emerged as a potential solution to the global energy problem as well as to the ever-increasing and diverse consumer demands. To this end, more flexible dispersed production units are involved, mainly based on renewable energy sources (RESs). Another key novelty of SGs is their ability to gather information directly from consumers and production units in real time, thus facilitating optimum network planning and recovery as well as minimization of outage probability. Hence, it is important to use appropriate advanced infrastructure, which, in combination with modern telecommunication networks, will enable the full exploitation of SGs. In this context, to make the electricity system more efficient, avoid voltage and frequency imbalance issues and implement optimal production and consumption planning, LF is a vital process and plays a key role in the management of future electricity systems. Therefore, recent state-of-the art approaches in LF methods are also presented and discussed. In the same context, current limitations and proposals for future work are identified as well.

Telecom doi: 10.3390/telecom7040092

Authors: Ling He Qingrui Guo Xuerang Guo Huiting Yang Yanan Xin

In wireless networks with severe blockage, path loss critically limits communication coverage. Reconfigurable Intelligent Surfaces (RIS) offer a promising remedy. However, the fine-grained control of massive reflecting elements incurs prohibitive computational overhead, which hinders real-time deployment. To address these challenges, this paper proposes a low-complexity scheme integrating RIS block selection with adaptive beamforming. The large-scale RIS is partitioned into multiple sub-arrays to enable block-wise phase control. By activating only those blocks with dominant channel gains, the system maximizes reflection gain while minimizing control overhead. To avoid the exponential complexity of exhaustive search, we develop a deep neural network (DNN)-based prediction architecture. By learning the mapping from channel states to optimal configurations, the DNN enables instantaneous selection of near-optimal RIS block combinations. Simulation results show that the proposed data-driven scheme achieves near-optimal bit error rate (BER) performance compared to exhaustive search. Notably, it avoids the exponential complexity growth typically associated with an increasing number of reflecting elements. The proposed mechanism extends reliable coverage range and improves link stability, offering an efficient solution for future wireless networks.

Telecom doi: 10.3390/telecom7040091

Authors: Keze Li Olaoluwa Popoola Yusuf Sambo

5G electromagnetic field (EMF) measurement is more challenging than measurement in previous cellular generations because 5G New Radio uses time-division duplexing, flexible bandwidths, beam sweeping, massive MIMO, and user-specific traffic beams. As a result, the measured synchronisation signal block (SSB) or PBCH-DMRS level may not directly represent the maximum exposure produced by data transmission. This motivates a combined tutorial and structured survey of existing 5G EMF measurement studies and procedures. This paper reviews the literature on 5G EMF measurement by classifying existing methods into frequency-selective measurement, code-selective measurement, actual exposure assessment, maximum-exposure extrapolation, and network-counter-based assessment. Representative field studies, public measurement reports, and network-data-based studies are compared according to their measurement scenarios, exposure objectives, and limitations. The paper further discusses key uncertainty sources, including beam/gain offset, TDD duty cycle, bandwidth extrapolation, traffic variation, spatial sampling, and equipment-related uncertainty. Finally, open challenges related to FR2 millimetre-wave measurements and reconfigurable propagation environments are discussed. By combining tutorial background with a structured survey, this paper clarifies 5G EMF measurement procedures, maximum-exposure extrapolation, uncertainty sources, and FR2 millimetre-wave measurement challenges.

Telecom doi: 10.3390/telecom7040090

Authors: Zohreh Moghaddas Nasim Roudabr Shimo Zhang Waseem Afzal

In most production systems, the objective is to minimize input consumption while maximizing the generation of desirable outputs. However, many production processes also generate undesirable outputs as by-products of desirable outputs. In many real-world systems, undesirable outputs are inherently linked to the production of desirable outputs. Several studies in the Data Envelopment Analysis (DEA) literature have addressed performance evaluation of decision-making units (DMUs) in the presence of undesirable outputs. However, most existing models assume that desirable and undesirable outputs are independent, which may not reflect real production environments. The objective of this study is to model the dependency between desirable and undesirable outputs and to develop a novel DEA framework based on an axiomatic approach. Specifically, the classical axiom of output disposability is decomposed into two separate axioms: disposability of desirable outputs and disposability of undesirable outputs. Based on these axioms, a new production possibility set (PPS) is constructed. The proposed DEA model explicitly incorporates the dependency between desirable and undesirable outputs. A case study involving sensor monitoring systems is presented to demonstrate the applicability of the proposed approach.

Telecom doi: 10.3390/telecom7040089

Authors: Pingyuan Zhang Limin Wang

Vehicular Ad Hoc Networks (VANETs) are mobile networks that offer new services and communication between moving vehicles, roadside infrastructure, and a trusted authority. With the development of autonomous and connected vehicles, the issue of authentication in VANETs has become increasingly prominent due to the lack of mutual trust among network entities. However, standard authentication models for VANETs must account for total computational and communication overhead, regardless of the timing of authentication message generation. To address this limitation, this work proposes an advanced authentication paradigm for VANETs called the online/offline VANET framework, and formalizes this novel framework to realize lightweight authentication by shifting heavy computational overhead to the offline phase. The proposed model is divided into an offline phase and an online phase. In the offline phase of the free time before the message becomes available, it allows more powerful trusted authority to pre-compute, and in the online phase, resource-constrained devices only execute a small set of residual operations. Based on this model and a new identity-based signature, we give an efficient instantiation and use a mobile platform to evaluate it. The experimental results demonstrate that our construction achieves low online computational and communication overhead.

Telecom doi: 10.3390/telecom7040088

Authors: Moubarek Traii Zied Harouni Mohamed Glaoui Said Ghnimi Ali Gharsallah

This paper presents a novel optimal control-based beamforming framework for phased antenna arrays, targeting advanced wireless communication and radar applications, including 5G systems. Unlike conventional beamforming techniques, such as Fourier-based methods and adaptive algorithms (e.g., LMS and RLS), the proposed approach formulates the beam synthesis problem as a discrete-time optimal control problem. The antenna array is modeled using a state-space representation, and a quadratic cost function is introduced to jointly minimize the deviation from a desired radiation pattern and the excitation power. The optimal excitation weights are derived using the Linear Quadratic Regulator (LQR) framework by solving the discrete-time algebraic Riccati equation. This formulation enables an effective trade-off between sidelobe suppression, main lobe accuracy, and power efficiency. Simulation results demonstrate that the proposed method achieves a well-focused main beam, significantly reduced sidelobe levels, and improved directivity compared to conventional approaches. Furthermore, the framework offers robustness and computational efficiency, making it a promising candidate for future FPGA and embedded implementations. Overall, the proposed optimal control-based beamforming approach provides a flexible, robust, and computationally efficient solution for next-generation antenna systems in 5G, beyond-5G (B5G), and radar applications.

Telecom doi: 10.3390/telecom7040087

Authors: Youssef El Maimouni Faouzi Rahmani Saida Ahyoud Abdelmoumen Kaabal

In this paper, a mechanically reconfigurable antenna is proposed to overcome the limitations of conventional patch antennas, particularly their static radiation patterns in millimeter-wave (mmWave) 5G applications. The proposed design integrates a physically rotating metasurface above a compact patch antenna, enabling dynamic beam steering through a simple mechanical rotation. A key contribution of this work is the clear and highly predictable relationship between the metasurface rotation angle and the resulting main lobe direction. By rotating the metasurface to specific positions, the main beam is precisely steered to 0∘, 90∘, 180∘, and 270∘ in direct correspondence with the metasurface rotation angle. For clarity and conciseness, four representative rotation states are selected and analyzed in this work, although the proposed antenna inherently supports continuous beam steering as a function of the metasurface rotation angle. Full-wave electromagnetic simulations, utilizing a RT/Duroid 5880 substrate, confirm a resonance frequency at 28 GHz with a bandwidth of 1.7 GHz, covering the frequency range from 27.15 GHz to 28.85 GHz. The results confirm notable performance improvements, with the antenna achieving a maximum realized gain of 8.66 dBi and its radiation efficiency increasing from 90% to 94% after metasurface integration. The proposed antenna offers a compact structure, high efficiency, and reliable beam steering without the need for complex feeding networks or active components, making it a promising solution for next-generation wireless communication systems.

Telecom doi: 10.3390/telecom7040086

Authors: Elton Boshnjaku Galia Marinova Edmond Hajrizi Besnik Qehaja

Smart microgrids combining photovoltaic arrays, wind turbines, and battery storage generate telemetry that existing open-source monitoring tools cannot process with per-mechanism energy loss visibility in real time. This paper presents the design, implementation, and evaluation of an IoT monitoring framework. The framework incorporates a physics-based microgrid simulator, a hierarchical MQTT communication architecture, and a React-based web-based user interface that supports WebSocket-based real-time data visualization. The framework consists of ten containerized microservices that can be started with a single command: docker compose up -d. All stack performance testing was conducted using a simulated 1 h test case based on a 100 kWp PV system, 10 kW wind turbine, and 50 kWh battery-powered campus microgrid. Median P50 publisher-to-subscriber latency was 27.2 ms and 99th percentile (P99) latency was 48.3 ms, with 100% message delivery across 5840 test messages, with per-topic analysis revealing a 25 ms serialization-order effect in sequential MQTT publishing. Comparative analysis against nine existing platforms including OpenEMS, VOLTTRON, Eclipse Ditto, and pymgrid confirms that, among the platforms surveyed, none unifies physics-based loss telemetry, IoT communication, time-series storage, and real-time visualization in a single reproducible deployment.

Telecom doi: 10.3390/telecom7040085

Authors: Chengyong Yang Xuanlong Ruan Jianlin Cheng

Cloud computing and mobile edge computing address the growing demand for computing power driven by the rise in data-intensive applications, but they are prone to creating computing silos, resulting in unbalanced resource utilization. To address this issue, the computing power network (CPN) has been introduced to enable the centralized management and scheduling of resources across the entire network. However, task scheduling in the CPN requires joint selection of computation nodes and routing paths, which greatly increases the complexity of the scheduling problem. In existing studies, heuristic methods are difficult to satisfy real-time requirements, whereas deep reinforcement learning methods ignore the collaborative optimization of network resources, making them difficult to adapt to complex CPN scenarios. To this end, we propose a task scheduling method for the CPN, called TS-DQNF. First, the method uses the Deep Q-Network (DQN) to determine the computation node for the computation task. Then, it introduces a dynamic congestion-aware mechanism to determine a low-cost routing path. Finally, it gradually obtains an effective task scheduling scheme through multiple rounds of alternating iterations. Simulation results show that the TS-DQNF improves the task success rate by 2.47–60.71% and reduces the average processing delay by 1.92–16.94% compared with other methods, while demonstrating good convergence performance.

Telecom doi: 10.3390/telecom7040084

Authors: Edgar Hernan Rosas Espinosa Mario Eduardo Rivero Ángeles Ricardo Menchaca Méndez

The increasing frequency of social and emergency situations in modern cities has exposed the limitations of traditional cellular networks, which are often designed based on average traffic demands. These networks struggle to handle sudden demand peaks, leading to service blockages and degraded quality of service. To address this issue, the use of Unmanned Aerial Vehicles (UAV) as mobile base stations has been proposed as a temporary solution to expand network capacity during high-demand periods. However, existing traffic models, such as Erlang-B, fail to capture the dynamic entry, exit, and variability of dwelling times associated with UAVs, limiting their accuracy in real-world scenarios. To overcome these challenges, this work proposes the Erlang-U model, which extends classical traffic analysis by incorporating Markov chains and combining Erlang and Hyperexponential distributions to accurately model the heterogeneous and dynamic nature of UAV sojourn times. This novel approach enables both analytical and computational modeling of UAV mobility and dynamic availability, providing a more realistic estimation of blocking probabilities in cellular networks. Simulation results demonstrate that the adaptive deployment of UAVs, guided by the proposed model, can reduce blocking probability by over 25% compared to conventional solutions. These findings highlight the importance of selecting appropriate sojourn time models to optimize network resilience and efficiency in dynamic and high-demand environments.

Telecom doi: 10.3390/telecom7040083

Authors: Rivana El Hajj Chehade Elias Rachid Sawsan Sadek Georges Zakka El Nashef

This paper presents a corrugated Vivaldi antenna architecture targeting sub-6 GHz and millimeter-wave frequency bands for 5G CubeSat applications, combining experimental validation at sub-6 GHz with a simulation-based scaling study at 26.5 GHz. Existing CubeSat antenna designs either target a single frequency band or rely on complex metamaterial structures incompatible with nanosatellite fabrication constraints. To address this gap, a single-element corrugated Vivaldi antenna measuring 90 mm × 80 mm is designed, fabricated on FR-4 substrate, and experimentally validated at 3.5 GHz, confirming a wide impedance bandwidth of 2.75 GHz and a peak gain of 9.6 dBi. The strong agreement between CST Studio Suite simulations and measurements validates the electromagnetic solver configuration, which is subsequently applied, as a simulation-based design study, to a geometrically scaled version on Taconic RF-60A substrate operating at 26.5 GHz. The miniaturized single-element version achieves a simulated 17 GHz ultra-wideband response and 6 dBi gain in a 7.32 mm × 6.32 mm footprint. Two- and four-element array configurations at 26.5 GHz demonstrate systematic simulated gain progression to 9 dBi and 13 dBi, respectively, with beamwidth narrowing from 49° to 30°. All 26.5 GHz designs are simulated with lossy copper metallization (σ=5.8×107 S/m) and are entirely simulation-based; experimental mmWave validation is a designated target for future work. These results establish a validated design and scaling roadmap for corrugated Vivaldi antennas spanning sub-6 GHz and millimeter-wave bands, offering a cost-effective and CubeSat-compatible solution for high-data-rate inter-satellite communication links.

Telecom doi: 10.3390/telecom7040082

Authors: Udara Jayasinghe Anil Fernando

Preserving perceptual quality and structural fidelity during image transmission remains challenging under bandwidth constraints and noisy channel conditions. Conventional compression standards often exhibit significant performance degradation under severe channel impairments, while integrated quantum-inspired compression and transmission frameworks remain largely underexplored. To address these limitations, this work proposes a simulation-based quantum-inspired image transmission framework that combines Quantum Chirp Transform (QCT)-based compression with a multi-stage U-Net reconstruction and denoising mechanism. In the proposed framework, image bitstreams are encoded using variable-dimensional representations with encoding dimension k, transformed into a chirp-structured domain, and transmitted through a numerically simulated composite quantum noise channel. The QCT exploits non-stationary quadratic phase characteristics to achieve efficient compression while preserving structurally significant image information. At the receiver, inverse processing and adaptive multi-stage U-Net enhancement are employed to suppress channel-induced distortions and improve reconstruction quality. Simulation results demonstrate compression ratios ranging from 2:1 to 128:1 depending on the selected encoding dimension, while maintaining high reconstruction fidelity. Compared with quantum Fourier transform (QFT) compression under identical transmission conditions, the proposed framework achieves superior robustness under noisy channels, with PSNR improvements of up to 4.9 dB over a QFT-based baseline and classification accuracy improvements from 84.3% to 90.4% at 10 dB SNR. Results further show that higher-dimensional encoding improves compression efficiency but increases sensitivity to channel impairments, which is effectively mitigated by the proposed multi-stage U-Net reconstruction strategy. These findings demonstrate the potential of chirp-structured quantum-inspired representations for robust image compression and transmission in bandwidth-constrained environments.

Telecom doi: 10.3390/telecom7040081

Authors: Damien Rossi Sina Keshvadi Yogesh Sharma

Path-aware networking architectures, such as SCION, give endpoints explicit visibility into multiple inter-domain paths, opening new opportunities for data-driven path selection, reliability prediction, and automated diagnosis. However, the lack of standardized, machine learning-ready datasets collected from live path-aware deployments has slowed progress in this domain. We present ScionPathML, an open-source measurement and data-standardization pipeline that abstracts the complexity of SCION’s tooling to continuously collect longitudinal performance measurements (RTT, packet loss, jitter, bandwidth, and per-hop latency) in formats directly usable by ML pipelines. Using a four-week, multi-region campaign across four vantage points on the SCIONLab testbed, we release a public dataset capturing path availability, churn, lifetimes, and end-to-end performance across concurrently available paths. To demonstrate its application, we define four reproducible benchmark tasks, including short-horizon performance forecasting, path failure prediction, anomaly detection, and multi-objective path recommendation, each accompanied by baseline models and evaluation protocols. Our results show that live SCION path performance exhibits an exploitable temporal structure, enabling accurate short-term predictions and early detection of availability drops. Together, the dataset, benchmarks, and open tooling substantially lower the barrier for ML researchers and provide a reproducible foundation for accelerating innovation in path-aware networking.

Telecom doi: 10.3390/telecom7040079

Authors: Takayuki Matsumuro Satoru Shimizu Susumu Ano Takashi Tomura

Air-core (donut-shaped) microwave beams are attractive for wireless power transfer (WPT) for drones because their central intensity null can reduce field concentration near mission equipment mounted near the drone center. This paper proposes a fixed air-core beam WPT architecture in which the transmitting beam is not electronically steered; instead, the drone maintains its position near an efficient receiving region using onboard control based on relative beam-position information inferred from received signals. To support this architecture, we present a theoretical analysis of captured power and spillover for a circular receiving aperture illuminated by a Laguerre–Gaussian (LG) beam. Rather than claiming a direct extension of the modified Friis formula to LG beams, we derive a closed-form expression corresponding to the edge-based efficiency/spillover interpretation used in Gaussian-beam WPT discussions. We then report staged experimental validation using a 24 GHz radial line slot antenna (RLSA)-based air-core beam transmitter with a 25 W class feed circuit, a horn-antenna-based reference receiver for principal validation, and a panel rectenna prototype for implementation-oriented evaluation. The results clarify practical operating conditions and implementation limitations, including distance-dependent position-detection behavior and compact-receiver sensitivity degradation under air-core beam illumination.

Telecom doi: 10.3390/telecom7040078

Authors: Anastasios Papazafeiropoulos

Conventional multiple-input multiple-output (MIMO) systems rely on static antenna placement. To exploit additional spatial degrees of freedom, the fluid antenna (FA) concept has emerged as a promising solution for improving data rates and diversity performance. Most existing FA studies assume Rayleigh fading, whereas analytical characterization under Nakagami-m fading is more challenging. This article investigates the ergodic capacity of FA-assisted MIMO systems over Nakagami-m fading channels. By applying majorization theory, upper and lower bounds on the ergodic capacity are derived. High signal-to-noise ratio (SNR) approximations are then obtained to clarify the role of the fading parameter and the number of propagation paths. The large-system behavior is also studied, and Monte Carlo simulations are used to assess the tightness of the proposed bounds. The results show that the upper bound closely tracks the simulated capacity, while the lower bound remains useful mainly in the low-SNR regime.

Telecom doi: 10.3390/telecom7040080

Authors: Gerasimos Papanikolaou-Ntais Dionysios N. Sotiropoulos Athanasios Kanavos Alexandros Kaloxylos

5G New Radio Vehicle-to-Everything (NR V2X) networks must support heterogeneous traffic with strict and diverse latency requirements. Conventional proportional-fair (PF) scheduling does not explicitly account for packet deadlines, which can lead to deadline violations for critical vehicular services under congestion. This paper studies deadline-aware MAC scheduler-weight adaptation for 5G NR V2X using probabilistic prediction and reinforcement learning. We implement a closed-loop ns-3/5G-LENA framework in which network telemetry is exchanged with a Python control agent through ns3-ai shared memory. Gaussian Mixture Model (GMM), Hidden Markov Model (HMM), and Bayesian Logistic Regression (BLR) classifiers are used to predict imminent deadline violations. Their outputs are either mapped directly to scheduler weights or provided as additional state information to a Proximal Policy Optimization (PPO) agent. We evaluate ten scheduling strategies: PF, a non-learning Slack-Based Deadline-Aware Scheduler (SB-DAS), three classifier-only controllers, three classifier-assisted PPO variants, PPO-only, and PPO-only with safety shielding. Experiments are conducted across three vehicle densities and three random seeds per density, using the Deadline-Constrained Packet Reception Ratio (DC-PRR) as the main metric. The PF baseline achieves 61.55% mean DC-PRR and degrades from 75.2% at 30 vehicles to 44.1% at 60 vehicles. In contrast, all adaptive strategies exceed 95% mean DC-PRR and recover 34–38 percentage points over PF in every paired density/seed comparison. The main result is therefore the robust gap between PF and deadline-aware adaptation. Differences among the adaptive controllers are much smaller and fall within the observed seed-to-seed variability. In particular, SB-DAS, which uses no classifier, neural network, or training, achieves DC-PRR statistically indistinguishable from the learned and probabilistic controllers. This indicates that, in the evaluated scenarios, most of the gain comes from deadline awareness itself rather than from learning. We also find that adding classifier-derived violation probabilities to PPO does not consistently improve performance over PPO using raw telemetry alone. To support reproducibility and deployment assessment, the paper includes detailed parameter tables, reward-coefficient and sensitivity analysis, scheduler-weight sensitivity, and per-controller inference-latency and complexity measurements.

Telecom doi: 10.3390/telecom7030077

Authors: Adrian Benavides Leonardo Banegas Luigi O. Freire

The industrial Internet of Things (IoT) allows traditional electromechanical systems to be connected to digital monitoring and control platforms, especially when field devices use industrial protocols that must be integrated into web services without modifying their main operation. This work implements an IoT architecture based on the Open Systems Interconnection (OSI) model to interconnect two Variable Frequency Drives (VFDs) through a LOGO! Programmable Logic Controller (LOGO! PLC), a Human–Machine Interface (HMI), a ZLAN5143D gateway, Node-RED, Message Queuing Telemetry Transport (MQTT), and Adafruit IO. The communication integrates RS485/Modbus RTU at the field level and Modbus TCP/IP over Ethernet at the upper network level using the gateway as the protocol conversion element. The validation was performed through Modbus Poll, variable acquisition, MQTT publication, and web visualization. The results show local communication response, acquisition of frequency, voltage, current, and revolutions per minute (RPM), together with remote control of start, stop, frequency setpoint, and rotation direction. The architecture is presented as a modular solution for electromechanical applications with IoT projection.

Telecom doi: 10.3390/telecom7030076

Authors: Pardeep Kumar Amit Kumar Sanjeev Kumar

Unauthorized plucking of flowers, fruits, and vegetables from residential plant beds is a recurring concern in urban and semi-urban household, causing damage to gardening resources, economic loss and inconvenience to sustainable gardening. To address this issue, the present study proposes an IoT-enabled Smart Residential Plant Bed Protection System (SRPBPS), which is the integration of motion sensors, plant disturbance sensors, a video monitoring unit, a microcontroller, a communication module, and an alarm mechanism for real-time intrusion detection and monitoring. The behaviour of the proposed system is analyzed using a continuous-time Markov modelling approach by considering various operational and failed states of system components. Important reliability measures, including system reliability, mean time to system failure (MTTF), and the expected number of failures over time, are evaluated analytically. In addition, sensitivity analysis of reliability and MTTF are carried out to identify the critical components influencing overall system performance. The obtained results provide useful insights into component-level impact on system effectiveness and support reliability-oriented design enhancement. The proposed framework contributes toward the development of intelligent, secure, and sustainable residential plant bed protection systems for modern residential environments.

Telecom doi: 10.3390/telecom7030075

Authors: Nokwanda Shezi Bakhe Nleya Beverly Pule

Converging millimetre-wave (mmWave) radio access with passive optical network (PON) fronthaul under the Open RAN (O-RAN) architecture promises unprecedented capacity for beyond-5G and 6G systems. Yet today, dynamic bandwidth allocation (DBA) in the PON and physical resource block (PRB) scheduling in the mmWave RAN operate independently, a critical design flaw that causes severe latency accumulation, resource fragmentation, and consistent failure to meet the divergent quality-of-service requirements of network slices. This paper breaks that deadlock by introducing the first slice-aware, computationally efficient orchestration framework that jointly optimises DBA and PRB allocation in a converged mmWave-PON O-RAN. We formulate the problem as a constrained Markov decision process (CMDP) with explicit latency, reliability, and throughput constraints for URLLC, eMBB, and mMTC slices. The core technical advance is a reward-shaped proximal policy optimisation (RS-PPO) algorithm whose potential-based shaping function directly penalises DBA–PRB misalignment and dense feedback on queue build-up, accelerating learning without compromising optimality. To make this work in near-real time on the O-RAN RIC, we embed three complementary efficiency engines: graph convolutional network (GCN) state abstraction, action masking, and prioritised N-step replay. Extensive 3GPP-compliant simulations show that RS-PPO slashes URLLC end-to-end latency by 37% (from 1.38 ms to 0.87 ms), boosts PRB utilisation by 28% (from 68% to 87%), and delivers 99.999% reliability, all while converging 45% faster and cutting inference time by 45% (to just 2.3 ms). The result is a sub-5 ms control cycle, compatible with O-RAN specifications and deployable as an xApp on the near-RT RIC. Our framework closes a long-standing coordination gap left unresolved by prior art, enabling true slice-aware convergence between the optical and wireless domains.

Telecom doi: 10.3390/telecom7030074

Authors: Shuo Yu Ahmed S. Khwaja Waleed Ejaz Alagan Anpalagan

With the rapid proliferation in communication devices and the expansion of applications, future sixth-generation (6G) networks are expected to enable a truly connected world. They will allow large-scale use cases, such as the Internet of Things (IoT) and unmanned aerial vehicles (UAVs), providing significantly faster and more innovative services ubiquitously. However, challenges remain, particularly in security. The growing number of devices and increased connectivity may lead to a larger attack surface. Many emerging technologies are actively addressing these security and privacy concerns, ensuring that we can benefit from the advantages of 6G networks and applications without falling victim to malicious attacks. In this paper, we conduct a comprehensive literature review of emerging technologies for secure communication in 6G networks, including artificial intelligence (AI) and machine learning (ML), blockchain technology, quantum-safe communication, and physical-layer security. First, we discuss the architecture of 6G networks from a security perspective. Second, we review existing surveys on 6G security issues and provide a quantitative analysis to identify research gaps, including technology-driven silos and domain fragmentation. Third, we develop a hierarchical taxonomy of security challenges and attacks in 6G networks, covering physical-layer attacks, network-level threats, device vulnerabilities, data privacy concerns, and emerging application-specific risks. We then examine the roles of key enabling technologies and present a mapping between security threats and corresponding technological solutions, along with a unified evaluation framework to facilitate cross-technology comparison. Furthermore, we propose an integrated multi-technology security framework and discuss practical deployment challenges by bridging the gap between simulation-based studies and real-world implementations. Finally, we outline concrete future research directions for advancing secure 6G communication systems.

Telecom doi: 10.3390/telecom7030073

Authors: Tarek Ali Panos Kostakos Saeid Sheikhi

Machine learning (ML) methods for network anomaly detection are emerging as effective proactive strategies in threat hunting, substantially reducing the time required for threat detection and response. However, the challenges in training and maintaining ML models, coupled with frequent false positives, diminish their acceptance and trustworthiness. In response, Explainable AI (XAI) techniques have been introduced to enable cybersecurity operations teams to assess alerts generated by AI systems more confidently. Despite these advancements, XAI tools have encountered limited acceptance from incident responders and have struggled to meet the decision-making needs of both analysts and model maintainers. Large Language Models (LLMs) offer a unique approach to tackling these challenges. Through tuning, LLMs have the ability to discern patterns across vast amounts of information and meet varying functional requirements. In this research, we introduce the development of HuntGPT, a specialized intrusion detection dashboard created to implement a Random Forest classifier trained utilizing the KDD99 dataset. The tool incorporates XAI frameworks like SHAP and Lime, enhancing user-friendliness and intuitiveness of the model. When combined with a GPT-3.5 Turbo conversational agent, HuntGPT aims to deliver detected threats in an easily explainable format, emphasizing user understanding and offering a smooth interactive experience. We investigate the system’s comprehensive architecture and its diverse components, assess the prototype’s technical accuracy using the Certified Information Security Manager (CISM) Practice Exams, and analyze the quality of response readability across six unique metrics. Our results indicate that conversational agents, underpinned by LLM technology and integrated with XAI, can enable a robust mechanism for generating explainable and actionable AI solutions, especially within the realm of intrusion detection systems.

Telecom doi: 10.3390/telecom7030072

Authors: Yuxin Xia Yuanchao Yan Xianzhong Li Yandong Zhao Weimin Liu Tianhao Guo

Wireless links in underground coal mines suffer from severe attenuation, blockage, and limited spatial coverage. To improve link quality under these conditions, we study a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted system with multiple movable antennas (MAs) installed at the base station (BS) panel. Unlike prior models that assume a continuous movement box, we explicitly account for practical panel constraints: mechanical supports and RF feed lines partition the BS panel into non-overlapping irregular feasible subregions. This turns the BS-side antenna-positioning task into a mixed-integer nonlinear program (MINLP). We formulate a joint optimization problem that couples BS beamforming, STAR-RIS transmission/reflection coefficients, BS-side MA positions, and MA-to-subregion assignment with collision-avoidance constraints. To solve it, we adopt a block coordinate descent (BCD) framework: successive convex approximation (SCA) for beamforming, semidefinite relaxation (SDR)-based updates for STAR-RIS coefficients, and a penalty-based continuous relaxation for MINLP handling. The MA solver further integrates Hungarian initialization, cross-region jump updates, and reassignment corrections to escape poor local subregions. Simulation results in coal mine channel settings show that the proposed method yields a 66.7% sum-rate gain over fixed-antenna baselines and reduces required transmit power by 16.8 dB at the target-rate operating point. Compared with a regular-region BS-MA baseline, the irregular-partition design achieves an additional 5.6 dB power saving, demonstrating the practical value of hardware-aware geometry modeling.

Telecom doi: 10.3390/telecom7030071

Authors: Chen Xu Song Wen Ting Lyu Donghong Qin

This paper investigates an RIS-assisted mobile edge computing (MEC) system without reliable direct links between users and base stations (BSs). Users offload tasks to BSs through reconfigurable intelligent surface (RIS)-reflected links, where offloading decisions, service prices, and RIS-assisted transmission quality are tightly coupled. We formulate a joint design problem that considers task latency, transmission energy consumption, service pricing, BS computing constraints, and RIS phase-shift constraints. The RIS phase shifts are first optimized to improve the effective cascaded channel gain. Then, a distributed price-negotiation-based offloading mechanism is developed to coordinate user association and service pricing under channel-dependent utilities. Analysis and simulations show that the proposed algorithm converges within a finite number of iterations and achieves a balanced tradeoff between user utility and BS revenue.

Telecom doi: 10.3390/telecom7030070

Authors: Tadele A. Abose Thomas O. Olwal

This study proposes a novel hardware-impairment-aware convolutional neural network (CNN)-based hybrid precoding scheme for cell-free massive multiple input multiple output (MIMO) systems operating in the terahertz (THz) band under practical constraints of imperfect channel state information (CSI) and transceiver hardware non-idealities. In a realistic THz simulation environment incorporating molecular absorption, phase noise, channel aging, and power consumption models, the proposed CNN precoder demonstrates significant performance improvements over conventional Zero-Forcing (ZF), Kalman, and Minimum Mean Square Error (MMSE) schemes. Quantitative results show that the CNN achieves spectral efficiency gains of 10.67% over Kalman, 14.67% over MMSE, and 70% over ZF for an eight-user scenario. In addition, the CNN-based precoder provides an SNR gain of 0.8 dB over MMSE and 2 dB over ZF. Complexity analysis indicates that the CNN approach is 17% less complex than ZF, 44% less complex than Kalman, and 60% less complex than MMSE. Further analysis of individual impairment effects reveals that the CNN effectively mitigates the compounded degradation caused by hardware distortions and CSI imperfections, exhibiting only a 25% performance loss compared to an ideal hardware baseline. These results establish the proposed data-driven precoder as a robust, computationally efficient, and high-performance solution for reliable and energy-sustainable ultra-high-throughput THz communication networks.

Telecom doi: 10.3390/telecom7030069

Authors: Omolara Ogundipe Abimbola Fisusi Funmilayo B. Offiong Akinbode A. Olawole Enoruwa Obayiuwana

Cell outages are conventionally mitigated through cell outage compensation, but national roaming frameworks offer alternative solutions with extra cost and energy efficiency benefits. Most national roaming studies evaluate performance over aggregate subscriber populations with limited focus on suburban environments or explicit resource block partitioning for protection of primary users’ quality of service (QoS). We propose a Resource-Constrained Temporary Roaming (RCTR) scheme that grants roaming users access to only a fraction C of the resources of visited operators during a cell outage to protect the QoS of primary users. System-level simulations in a suburban macrocell compare the blocking probability, throughput, and spectral efficiency of the RCTR scheme with two baseline schemes. Simulation results show the RCTR scheme balances relief for roaming users with protection of primary users’ QoS better than the baseline schemes and reduces 100% blocking probability under a No Roaming approach to below 10% at low traffic loads. For the aggregate population, performance metrics improve with increasing C, but exhibit diminishing returns as C approaches one. While primary users can tolerate high C values at low traffic loads, their QoS worsens with increasing C at higher loads. Hence, QoS protection depends on the appropriate selection of C across traffic intensities.

Telecom doi: 10.3390/telecom7030068

Authors: Miriam Litz Xesspe Carlos Pedrito Ccorahua Jose E. Velazco Pablo Raul Yanyachi

This work presents the design, simulation, fabrication, and practical evaluation of low-cost Cross-Yagi antennas for VHF/UHF satellite ground-station applications. The main contribution lies in the integrated development of a low-cost VHF/UHF antenna solution for a functional amateur satellite ground station, combining electromagnetic design, physical fabrication, and operational validation through real satellite signal reception. Two antennas operating at 145 MHz and 434 MHz were designed using Ansys HFSS, fabricated, and experimentally characterized by means of S11 and Smith chart measurements. Simulated results were used to evaluate gain, radiation characteristics, and circular-polarization behavior through axial-ratio analysis. The fabricated prototypes showed acceptable impedance performance close to the intended operating bands and a substantially lower material cost than representative commercial alternatives. Finally, the antennas were integrated into an amateur satellite ground station for real beacon reception and telemetry decoding, confirming the practical feasibility of the proposed approach for low-cost VHF/UHF satellite communication systems.

Telecom doi: 10.3390/telecom7030067

Authors: Yilin Zhou Zhongqi He Changjun Liu

Microwave wireless power transfer (MWPT) is a promising technology for powering dedicated industrial Internet of Things (IoT) devices, enabling battery-free operation. However, in realistic MWPT deployments, the received RF signals fluctuate drastically due to varying transmission distances and multipath fading. Additionally, the equivalent impedance of sensor nodes varies significantly during duty cycles, shifting between a low-resistance active state and a high-resistance sleep state. Consequently, maintaining high rectification efficiency under these dynamic conditions remains a critical challenge. This paper proposes a high-efficiency rectifier with a wide input power and load range based on the suppression of second and third harmonics. The rectifier adopts a dual-diode parallel configuration. By leveraging the impedance compensation characteristics of two short-circuited stubs with distinct electrical lengths, it simultaneously achieves fundamental-frequency impedance matching and harmonic suppression without the need for an additional matching network. Validated through theoretical derivation, simulation analysis, and physical prototype testing, the proposed 2.45 GHz rectifier realizes high-efficiency rectification over a wide dynamic range. Experimental results demonstrate that the power dynamic range reaches 10 dB when the rectification efficiency exceeds 70%, and extends to 17 dB when the efficiency is above 60%. Furthermore, the rectification efficiency is insensitive to load variations (100–1200 Ω), making it highly suitable for powering wireless sensor nodes with varying operating modes in complex electromagnetic environments.

Telecom doi: 10.3390/telecom7030066

Authors: Ahmed Ali Al-Masry Michael Ibrahim Hesham Elbadawy Hadia El-Hennawy Mehaseb Ahmed

The rapid increase in interest for Vehicle-to-Everything (V2X) networks has created significant challenges in efficient radio resource management. This paper addresses the problem of joint subcarrier assignment and power allocation to maximize the spectral efficiency of the system. First, this paper mathematically formulates resource allocation and power allocation as an optimization problem, which is solved using conventional optimization methodologies to establish a baseline for performance benchmarking. To overcome the high computational complexity associated with traditional optimization, we subsequently propose a Multi-Agent Deep Q-Network (Multi-DQN) agent framework based on deep reinforcement learning (DRL). The proposed agent learns optimal allocation strategies through interaction with the environment, enabling adaptive and real-time decision-making. The system performance is investigated in different environments under both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios, addressing a gap in prior approaches. Simulation results demonstrate that the proposed Multi-DQN agent approach significantly outperforms the enhanced conventional benchmark, achieving higher spectral efficiency (SE) while substantially reducing the computational complexity.

Telecom doi: 10.3390/telecom7030065

Authors: Obinna Okoyeigbo Xutao Deng Ray Sheriff Daniel Jeremiah Olamilekan Shobayo

Accurate channel estimation is essential for reliable wireless communication, yet it becomes significantly challenging in 6G due to extreme propagation conditions. Factors such as high mobility, large delay spreads, and low signal-to-noise ratios (SNRs) create environments where traditional estimators struggle to perform effectively. While deep learning (DL)–based channel estimation has emerged as an alternative approach, its advancement is hindered by the lack of standardized and reproducible datasets that follow 3GPP-compliant signal models and realistic receiver preprocessing. This paper introduces ChanEst, a reproducible dataset generation framework for DL-based channel estimation. The ChanEst dataset uses 3GPP-compliant physical-layer procedures, demodulation reference signals (DMRS), tapped delay line (TDL) channel models, and FR3 (Frequency Range 3) configurations, performing stratified random sampling of key channel parameters to ensure statistical diversity. Least squares (LS) estimates are obtained and interpolated across the time–frequency grid to construct practical receiver input tensors, while the corresponding labels are derived from the perfect channel responses produced by the channel model. The resulting datasets are stored as real-valued tensors suitable for DL models and accompanied by metadata logs to enable stratified evaluation and fair benchmarking. Comprehensive statistical analysis validates the dataset’s diversity and physical consistency, and a fully implemented DL baseline model demonstrates its practical machine learning utility by outperforming conventional estimators under severe channel impairments. The ChanEst dataset is publicly available on Mendeley Data, with full code provided on GitHub, enabling reproducible experimentation for DL-based 6G channel estimation.

Telecom doi: 10.3390/telecom7030064

Authors: Mouchira Bensari Azeddine Bilami Karam Eddine Bilami Pascal Lorenz Jaafar Gaber

The emergence of 6G heterogeneous networks integrating unmanned aerial vehicles (UAVs), intelligent reflecting surfaces (IRSs), Internet of Things (IoT) devices, and fog/edge nodes creates new opportunities for intelligent and latency-sensitive applications while introducing significant security challenges. Traditional authentication mechanisms are inadequate for such dynamic, distributed, and heterogeneous environments that require secure collaborative communications. This paper proposes an authentication scheme based on Fog-RAN (Fog Radio Access Network) and a dual-blockchain architecture with smart contracts and elliptic curve cryptography (ECC). The proposed scheme provides secure network access, mutual authentication, traceability, auditability, and zero-trust enforcement. Formal verification using the ROR model, AVISPA and performance evaluation through smart-contract simulations indicate resilience to common network and cryptographic attacks and improved efficiency. Compared with existing schemes, the proposed approach reduces computation cost, bandwidth, and energy consumption by 64.2%, 59.6%, and 31.4%, respectively. These results support the suitability of the scheme for secure, scalable, and energy-efficient authentication in next-generation 6G networks.

Telecom doi: 10.3390/telecom7030063

Authors: Blaž Valič David Plets Gunter Vermeeren Christos Apostolidis Peter Gajšek

Private 5G mobile networks are emerging as a platform for wireless connectivity in professional applications across smart industrial sectors such as automated warehousing, logistics, autonomous vehicle deployments in campus environments, mining, and material processing, among others. It is expected that most Machine-to-Machine (M2M) and Industrial Internet of Things (IIoT) communication links will increasingly rely on wireless solutions, as the flexibility they offer provides clear advantages over hard-wired network installations. To gain insight into workers’ exposure to radiofrequency electromagnetic fields (RF EMF) emitted by 5G private mobile networks, an analysis was conducted based on measured and calculated RF EMF levels from various 5G private networks in real-world scenarios across different smart industrial sectors and R&D platforms in three countries. Several exposure scenarios were evaluated, including production facilities, logistics operations, office environments, and research sites. The installations included different configurations: private standalone and non-standalone 5G networks operating at 3.5 GHz and 26 GHz, as well as public networks with private slicing. The results clearly demonstrated that exposure levels in all investigated scenarios were well below existing exposure limits. In a typical indoor industrial environment where pico 5G base stations are deployed, the measured exposure was found to be no greater than 0.006% of the Directive 2013/35/EU action value and 0.03% of the ICNIRP guideline limits for the general public.

Telecom doi: 10.3390/telecom7030062

Authors: Svetislav Savović Matija Savović Xiong Deng

Mode coupling in multimode step-index polymer optical fibers (SI POFs) plays a critical role in determining signal integrity and bandwidth performance in optical communication systems. It originates from intrinsic random perturbations that influence power distribution among propagating modes, making accurate prediction of steady-state distributions (SSDs) essential for reliable system design. In this work, we model mode coupling as a stochastic process using the Langevin equation, incorporating simulated Langevin forces to numerically evaluate modal power evolution and steady-state behavior. The proposed approach demonstrates strong agreement with previously reported experimental results, validating its capability to capture energy redistribution mechanisms induced by fiber imperfections. From a telecommunications perspective, the model provides valuable insights into modal dispersion, bandwidth limitations, and signal degradation in SI POF-based links. These results establish a robust and efficient framework for analyzing and optimizing multimode SI POFs, supporting their application in high-speed data transmission and short-reach optical communication networks.

Telecom doi: 10.3390/telecom7030061

Authors: Yueshun He Hao Song Ping Du Linlin He Xiaoyu Cao Yunzhe Liu Weiqian Song

Spectrum prediction is essential for implementing dynamic spectrum management and mitigating spectrum congestion. However, spectrum data in real electromagnetic environments exhibit high non-stationarity, multi-scale features, and complex non-Euclidean spatio-temporal coupling characteristics, which limit the prediction accuracy of existing models. To address these issues, this paper proposes an attention-based spectrum prediction method for spatio-temporal feature synergy (SSA-A-BiGCRNN). First, Singular Spectrum Analysis (SSA) is introduced to decompose and reconstruct the non-stationary spectrum signals, filtering out high-frequency burst noise and extracting core evolutionary trends. Second, a spatial topology graph among multiple frequency bands is constructed based on the Spearman rank correlation coefficient. A Bidirectional Graph Convolutional Recurrent Neural Network is then designed to simultaneously capture the spatial dependencies between frequency bands and the bidirectional evolutionary patterns in the time dimension. Finally, an attention mechanism is incorporated during the feature fusion stage to evaluate and focus on critical spatio-temporal information, further enhancing global prediction accuracy. Experimental results based on a real electromagnetic monitoring dataset demonstrate that the proposed model achieves an accuracy of 96.82%, a coefficient of determination (R2) of 0.9966, a Root Mean Square Error (RMSE) of 0.5597, and a Mean Absolute Error (MAE) of 0.4031, significantly outperforming existing models.

Telecom doi: 10.3390/telecom7030060

Authors: Hongliang Tian Bolin Song Xiaoke Liu

In response to the shortcomings of current mobile communication network (MCN) fault diagnosis methods, such as the insufficient robustness of time-series-spectrum features and the limited ability to capture long-distance dependencies, an improved convolutional neural network is proposed, along with a hybrid diagnosis method based on time-frequency perception and a lightweight deep network (TL-FDN). The TL-FDN introduces a time-series-spectrum feature enhancement module (TFN-E) at the input end, and enhances the robustness of features through a learnable Gabor filter bank. The main architecture employs a hybrid module that integrates a lightweight convolution (LiConv-Block) and a broadcast self-attention (BSA) mechanism (Former-Block), effectively balancing the efficiency of local feature extraction with the capture of global time-series dependencies. Additionally, the model uses a multi-task loss function to achieve joint diagnosis of fault type and fault location. The experimental results show that the average accuracy of the proposed TL-FDN method is 98.6%, which is 3.5% higher than that of the standard convolutional + standard attention baseline method. To strictly evaluate the performance improvement, this paper conducted a non-parametric Wilcoxon signed-rank test in 10 independent experiments. The p-values of the core model indicators were all strictly less than 0.05. These results statistically confirm the superiority of TL-FDN in the fault type identification and location tasks, while maintaining a lightweight parameter quantity suitable for edge-end deployment.

Telecom doi: 10.3390/telecom7030059

Authors: Jorge Duarte Carlos Serôdio Sílvia de Castro Pereira Fernando Santos António Valente Sérgio Ramos Sérgio Leitão

Regulations of next-generation networks (NGNs) have played a central role in the transition from copper networks to broadband networks in Fiber to The Home (FTTH), also allowing for a reduction in asymmetries between incumbent operators and their competitors. Despite common European Union directives, telecom infrastructure development varies across countries due to differences in regulation, investment models, legacy networks, operators’ behavior, and public policies. This study analyzes the evolution of Very High-Capacity Networks (VHCNs), focusing on the implementation of FTTH in four European countries (Portugal, Spain, France, and Germany), and in South Korea. The latter was included as a benchmark in government-driven broadband development. The analysis considers key factors influencing FTTH development, including infrastructure regulation, fiber investment incentives, incumbent strategies, infrastructure sharing and co-investment, rural coverage plans, and demographic differences of each country. The results show that regulatory measures directly influence the pace of FTTH installation; but its effectiveness also depends on investment incentives, market competition, and demand factors. Portugal, Spain, and France have high FTTH coverage despite different regulatory and investment models. In contrast, Germany relied on xDSL over copper networks for a long time, causing significant delays, with an FTTH coverage rate of 42.5% and a penetration rate of only 12.3%, putting the European Union’s 2030 goal of universal 1 Gbps coverage at risk. South Korea shows that long-term public policies, demand-side incentives, and high digital adoption accelerate mass FTTH adoption and turn telecommunications infrastructure into a key driver of economic and technological development.

Telecom doi: 10.3390/telecom7030058

Authors: Behar Haxhismajli Galia Marinova Edmond Hajrizi Besnik Qehaja

Smart microgrids depend on continuous communication between controllers, sensors, and actuators over industrial protocols like Modbus TCP, message queuing telemetry transport (MQTT), and distributed network protocol 3 (DNP3), which were designed without built-in security mechanisms. The gateway that aggregates this traffic represents a single point of failure and is vulnerable to distributed denial-of-service (DDoS) attacks. Most existing detection methods require labeled attack data for training, a condition rarely met in operational technology (OT) environments. This paper presents an unsupervised convolutional neural network–long short-term memory (CNN-LSTM) model trained exclusively on normal microgrid gateway traffic to predict the next traffic window; anomalies are flagged when the prediction error exceeds a threshold derived from the training distribution. A dual-branch architecture processes metric time-series through LSTM layers and flow aggregate features through CNN layers, fusing both representations for prediction. The model is evaluated against three protocol-specific DDoS attack scenarios—Modbus supervisory control and data acquisition (SCADA) flooding, MQTT publish storm, and DNP3 response flooding—none of which are seen during training. Compared against an isolation forest baseline and an autoencoder baseline under identical unsupervised conditions, the CNN-LSTM achieves higher precision and recall on all attack types. The framework is deployed within a web-based monitoring platform that supports real-time detection and anomaly logging.

Telecom doi: 10.3390/telecom7030057

Authors: Hamish Sturley Pablo Salva-Garcia Ahren Hart Leon Irving Chao Guo Muhammad Zeeshan Shakir

Fifth-generation (5G) mobile networks are widely positioned as key enablers of industrial digital transformation. However, despite extensive coverage expansion, the deployment landscape remains dominated by Non-Standalone (NSA) architectures integrated with legacy 4G cores, limiting the practical availability of advanced capabilities such as Ultra-Reliable Low-Latency Communication (URLLC), Massive Machine-Type Communication (mMTC), and network slicing. This has contributed to a disparity between projected 5G functionality and realised industrial utility. This paper investigates the economic and structural factors constraining advanced 5G adoption and examines their implications for emerging sixth-generation (6G) frameworks. We conceptualise the current stagnation as arising from concurrent supply-side and demand-side constraints: elevated Radio Access Network (RAN) capital expenditure relative to previous generations, and limited demonstrable return on investment (ROI) for advanced service capabilities. To evaluate these dynamics empirically, a regional stakeholder study was conducted across industrial and public sector organisations in Ayrshire, Scotland. Data were collected through structured surveys and workshop-based questionnaires involving 34 participants, with proportional sectoral analysis performed to assess representativeness. The results indicate that high initial deployment costs and ROI uncertainty are the primary adoption barriers, with 45.83% of respondents reporting no immediate operational requirement for advanced 5G features. The findings identify an implementation gap in which economic viability, rather than technical feasibility, limits progression beyond basic 5G deployment. The paper argues that unless cost-efficiency and sector-specific value articulation are addressed, similar adoption constraints may extend into 6G development. These results provide empirically grounded insights to inform more economically aligned next-generation network planning.

Telecom doi: 10.3390/telecom7030056

Authors: Zohaib Ijaz Fadhil Firyaguna Dirk Pesch

This article discusses the anti-skid braking control mechanism of aircrafts. Aircrafts use a sliding-mode controller (SMC) to generate the desired braking torque on its wheels to stop while landing. Potential runway variations and load differences on the wheels are considered, affecting the friction force on each wheel. Variations in the friction force generate drag torque, causing aircrafts to drift away from the runway. In order to counteract the drift, we propose a supervisory consensus controller, which adjusts the braking torque of each wheel to achieve equal force on each wheel. We consider a wireless communication channel between the supervisory controller and each wheel’s brake controller in an attempt to reduce cabling. As wireless communication needs to deal with potential communication losses that affect the overall control performance, a new control model that can accommodate communication losses has been devised. The proposed model is evaluated, and we demonstrate how well the consensus controller works over a noisy channel. Simulation results demonstrate that the proposed consensus-based control significantly improves braking performance, reducing drag torque and achieving up to 15–20% reduction in landing distance under 25% packet loss compared to baseline approaches.

Telecom doi: 10.3390/telecom7030055

Authors: Eduardo Vázquez Aldo Mendez Leopoldo A. Garza Alberto Reyna Gerardo Romero

Underwater wireless sensor networks (UWSNs) support critical applications such as environmental monitoring, offshore exploration, and surveillance; however, their performance is constrained by high propagation delay, limited energy resources, and node mobility caused by ocean dynamics. Many clustering approaches assume static nodes and use fixed-weight objective aggregation, which may reduce adaptability and lead to premature convergence. This paper proposes a cluster-head selection and cluster formation method for UWSNs based on a binary multi-objective Dragonfly Algorithm (BMDA-UWSN). The method considers energy consumption, acoustic latency, and load balance within a Pareto-based optimization framework, thereby reducing dependence on fixed-weight aggregation during the search stage. In addition, the Dragonfly-based optimization process uses dynamically adjusted coefficients to regulate the balance between exploration and exploitation while preserving solution diversity. To represent underwater node displacement, a semicircular mobility model with angular variation of ±45° is incorporated into the simulation scenario. Results obtained for a 100-node network show that BMDA-UWSN achieved better performance than Direct Transmission, LEACH, LEACH-C, SS-GSO, and CDFO-UWSN in terms of network lifetime, packet delivery, latency, and residual energy under the evaluated conditions. In particular, the first node dies at iteration 126 with BMDA-UWSN, compared with iteration 95 for CDFO-UWSN, while packet delivery increases by approximately 20% and latency decreases by about 5%. These findings suggest that BMDA-UWSN is a competitive clustering approach for underwater monitoring scenarios when evaluated under controlled node mobility conditions.

Telecom doi: 10.3390/telecom7030054

Authors: Alexander D. Dowell Mohamed Z. M. Hamdalla Kalyan C. Durbhakula

Designing an ultrashort, fast-rising high-power microwave (HPM) system requires an antenna that simultaneously provides ultrawideband (UWB) operation, high gain, and megawatt-level power handling under strict size, weight, and power (SWaP) constraints. To meet these requirements, this paper proposes an improved UWB HPM antenna that integrates a graded partial dielectric transformer (PDT) with a Koshelev-type combined antenna. The graded PDT improves impedance matching and field continuity by smoothing the dielectric-to-free-space transition, thereby alleviating a key bandwidth limitation of conventional combined antennas. Through iterative simulation, low-cost fabrication, and experimental validation, the proposed design achieves a 2.8x bandwidth enhancement, increasing the measured fractional bandwidth from 53% to 148%, with S11 < −10 dB from 0.5 to 3.0 GHz and with an additional −10 dB operating band from 3.5 to 4.4 GHz. Simulations predict a peak gain value of 15 dBi at 2.1 GHz. High-voltage pulsed tests (9–10 kV, 500 ps rise time) confirm robust operation, with radiated electric fields exceeding 10 kV/m at 1 m and no observable breakdown. The lightweight 3D-printed PLA structure (197 g) provides a scalable solution for directed-energy and electromagnetic-pulse applications.

Telecom doi: 10.3390/telecom7030053

Authors: Nakhoon Choi Heeyoul Kim

While integrating blockchain technology into Business Process Management (BPM) has gained attention, existing compilation-based approaches suffer from high redeployment costs and isolated network structures. This study proposes an FSM-based workflow interpreter engine utilizing the Inter-Blockchain Communication (IBC) protocol within the Cosmos ecosystem to overcome these limitations. The proposed system adopts an interpreter architecture that treats business logic as lightweight JSON specifications instead of hard-coding it into smart contracts. This separation allows for process updates through data modification rather than contract redeployment, significantly increasing operational flexibility. Furthermore, custom IBC packet structures were designed to enable seamless cross-chain process synchronization between independent application-specific blockchains. Experimental results demonstrate that the interpreter approach reduces process update costs by over 90% compared to conventional compilation methods. Additionally, gas consumption exhibited a linear growth pattern relative to task count and gateway complexity, ensuring cost predictability for large-scale business scenarios. Interoperability validation using a standard Procurement Order (PO) process showed successful cross-chain state transitions with a latency of approximately 1.45 s. This research provides a practical solution for building trust-based decentralized collaboration ecosystems by simultaneously achieving operational efficiency and interoperability in blockchain BPM.

Telecom doi: 10.3390/telecom7030052

Authors: Maria Gardano Antonio Nocera Michela Raimondi Ennio Gambi

Integrated Sensing and Communication (ISAC), enabling communication infrastructure to simultaneously transmit data and sense the surrounding physical environment, is emerging as a cornerstone technology for sixth-generation (6G) mobile networks. While these capabilities unlock new applications in healthcare, safety, and ambient intelligence, they also introduce novel ethical and societal challenges related to privacy, transparency, user autonomy, and trust, which are values fundamental to the social acceptance of the technology. Firstly, an overview of academic, institutional, and industrial contributions on human-centric 6G is provided, with a focus on how ethical values are addressed in ISAC-related contexts. Secondly, this paper reviews the distinctive characteristics of ISAC through representative human-centric use cases involving non-interactive and often invisible sensing of people, highlighting the ethical and societal implications emerging from such scenarios. By analyzing current standardization efforts and the scientific literature, this paper identifies emerging trends in Key Values (KVs) relevant to ISAC, as well as open research gaps that must be addressed to support trustworthy and value-oriented ISAC design in future 6G networks.

Telecom doi: 10.3390/telecom7030051

Authors: Chin-Ling Chen Wan-Jing Lee

Distributed Denial-of-Service (DDoS) attacks continue to escalate in scale and complexity, posing significant threats to modern network infrastructures and cloud services. Although many machine learning and deep learning approaches have been proposed for intrusion detection, most existing studies rely on raw traffic features and binary classification, which limits their ability to capture complex temporal characteristics of multi-class DDoS attacks. To address these challenges, this study proposes an ensemble stacking framework combined with a frequency-domain feature representation for DDoS detection using the CIC-DDoS2019 dataset. Random Forest (RF), AdaBoost, and XGBoost are employed as base learners, while Logistic Regression is adopted as the meta-learner, and grid search cross-validation is used to determine the optimal hyperparameters. The main contributions of this study are threefold. First, a feature extraction pipeline integrating Fast Fourier Transform (FFT), sliding-window segmentation, and SHA256-based deduplication is proposed to capture temporal–frequency characteristics of network traffic while reducing redundant feature segments. Second, a stacking ensemble model is constructed to integrate heterogeneous classifiers and improve classification robustness across multiple attack types. Third, the proposed framework significantly improves computational efficiency by reducing feature redundancy, leading to substantial reductions in model training time. Experimental results demonstrate that the proposed FFT + SHA256 + SW stacking model achieves near-perfect detection performance, with an accuracy of 0.9997 and an F1-score of 0.9998 on the original dataset, which further improves to an accuracy of 0.9998 and an F1-score of 0.9999 when combined with SMOTE. Statistical evaluation using the Friedman test confirms that the stacking model consistently achieves the best ranking among the evaluated classifiers. The results indicate that the proposed approach provides an accurate, efficient, and scalable solution for large-scale DDoS attack detection.

Telecom doi: 10.3390/telecom7030050

Authors: Rebin Saleh Balázs Villányi

Industrial Internet of Things (IIoT) systems generate high-dimensional, non-stationary sensor streams under strict memory and computational constraints, limiting the applicability of classical batch dimensionality reduction methods. While incremental PCA (IPCA) enables online updates, it produces dense components and lacks mechanisms for drift adaptation and interpretability. Existing sparse PCA methods, in contrast, are predominantly batch-oriented and unsuitable for streaming deployment. This paper presents incremental sparse adaptive PCA (ISAPCA), a unified streaming framework that integrates exponential forgetting for concept drift adaptation, mini-batch Oja–Sanger subspace tracking for online variance maximization, and proximal ℓ1 soft thresholding with QR re-orthonormalization for stable sparse component learning. The contribution lies in the coordinated implementation of these established mechanisms within a constant-memory architecture tailored to industrial edge and TinyML settings. We evaluate ISAPCA on three industrial datasets (SmartBuilding, Tennessee Eastman Process, and GasSensor) and compare it against streaming IPCA and offline upper-bound methods (randomized PCA, sparse PCA, and dictionary learning). ISAPCA retains approximately 93% and 96% of IPCA’s explained variance on SmartBuilding and Tennessee Eastman streams, respectively, while achieving improved explained variance on GasSensor (0.862 vs. 0.822 for IPCA, respectively). Across datasets, ISAPCA enforces sparse loadings without severe degradation in reconstruction fidelity. Ablation analysis confirms the necessity of both forgetting and sparsity components for stable performance under drift. Runtime measurements show sub-millisecond batch updates (0.234–0.606 ms for 256-sample mini-batches), demonstrating suitability for real-time deployment. These results indicate that ISAPCA provides a practical and interpretable solution for streaming dimensionality reduction in non-stationary industrial IoT environments, balancing variance retention, sparsity, and computational efficiency.

Telecom doi: 10.3390/telecom7030049

Authors: Yahya S. Junejo Faisal K. Shaikh Bhawani S. Chowdhry Waleed Ejaz

In the evolving landscape of next-generation wireless networks, ensuring seamless mobility and high-quality service delivery for millions of devices and end users in dynamic scenarios, where the speed of a wireless device keeps changing with time, is important. The mobility, seamless and continuous connectivity, and ultra-dense deployment of wireless networks pose a significant challenge. Seamless and successful transition of a wireless device from point A to point B in variable-speed scenarios is one of the major challenges in future networks. This paper presents a novel Deep Q-Network (DQN)-based reinforcement learning (RL) framework integrated with Software-Defined Networking (SDN) for intelligent mobility management in hybrid 5G cellular networks consisting of macro and small base stations. The proposed system architecture utilizes a SDN controller to receive real-time user measurement reports, including Reference Signal Received Power (RSRP), Signal-to-Interference Noise Ratio (SINR), and user velocity, thereby classifying user mobility into distinct subclasses and dynamically determining optimal handover parameters. Leveraging the DQN’s capability to learn adaptive strategies, the model enables seamless transitions between macro and small cells based on mobility profiles, thereby enhancing Quality of Service (QoS) metrics such as latency, throughput, and handover efficiency. Simulation results demonstrate consistent performance improvements over baseline and existing models in ultra-dense network environments, with handover success rates 10–15% higher across SINR and different speed scenarios, while maintaining a packet failure rate of 9% across different speed scenarios, allowing more users to transition during various environmental changes seamlessly. Our proposed model is compared with our previous work and Learning-based Intelligent Mobility Management (LIM2) models. Specifically, our previous work focused on adaptive handover management primarily for high-speed train scenarios using a learning-assisted approach tailored to fixed high-mobility scenarios, with a limitation to single mobility conditions. This work contributes to the field of merging SDN’s centralized control with the predictive power of RL, paving the way for more resilient and responsive mobile networks in high-mobility scenarios. The proposed approach incorporates subclass-based mobility action abstraction, joint optimization of TTT and hysteresis margin, and dynamic target cell selection using global network information available at the SDN controller.

Telecom doi: 10.3390/telecom7030048

Authors: Ahren Hart Hamish Sturley Paul Mclean Pablo Salva-Garcia Muhammad Zeeshan Shakir

The UK telecommunications sector’s 5G rollout is projected to consume 2.1% of national electricity by 2030, raising urgent sustainability concerns. This study empirically investigates, under controlled laboratory conditions, the energy performance and cost characteristics of two private 5G architectures—Vodafone’s Mobile Private Network (MPN) and an Open Radio Access Network (O-RAN) via BubbleRAN—and contextualises them against public network references and the United Nations Sustainable Development Goals (SDGs). Two complementary dimensions of energy performance are assessed: absolute power consumption (Watts), reflecting total system draw regardless of throughput; and throughput efficiency (Mbps/W), capturing useful data delivered per unit of energy. In terms of absolute power, O-RAN consumes less (460 W active, 378 W idle) than MPN (645 W active, 620 W idle). In terms of throughput efficiency, MPN delivers 1.45 Mbps/W versus O-RAN’s 0.44 Mbps/W under these specific controlled, single-cell conditions, a difference that reflects the tested hardware configurations (n77 vs. n78 band; 936 Mbps vs. 202 Mbps throughput; 2 × 2 vs. 4 × 4 MIMO) as much as any intrinsic architectural distinction. Both architectures offer substantially lower annual energy costs (£1060–£1486) compared to public micro-cells (£1991–£2666), representing 44–60% savings. Session continuity was 100% across all controlled trials; this reflects short-term laboratory conditions and should not be extrapolated to a long-term network availability guarantee without extended field validation. These results are configuration-specific preliminary indicators; the relative efficiency advantage of each architecture is expected to vary with load, band, and deployment scale. By 2030, UK 5G network operations are projected to generate 795,347–1,260,532 tonnes of CO2 annually across low-to-high demand scenarios; private deployment, by reducing site proliferation 15–33%, could displace a meaningful share of this footprint. These findings support SDGs 4, 8, 9, 12, and 13. Hybrid O-RAN–MPN pilots are recommended to maximise sustainability gains while advancing social equity and net-zero targets.

Telecom doi: 10.3390/telecom7020047

Authors: Rubén Juárez Fernando Rodríguez-Sela

At racing speeds above 300 km/h (≈83 m/s), hazard awareness becomes a vehicular-communications problem: 100 ms already correspond to about 8.3 m of blind travel before an alert can influence braking, line choice, or torque delivery. Cloud-only telemetry is therefore insufficient under intermittent coverage and variable round-trip delay, while conventional trackside and pit-wall links do not provide direct inter-bike hazard dissemination. We propose Hybrid Epistemic Offloading (HEO), an edge–mesh–cloud architecture for high-mobility V2V/V2X hazard dissemination that explicitly separates an ephemeral safety plane from a durable cloud-analytics plane. On-bike edge nodes ingest high-rate ECU/IMU signals over CAN and persist full-fidelity traces into standardized ASAM MDF containers, enabling loss-tolerant buffering, deterministic replay, and post hoc auditability across coverage gaps. For real-time safety, motorcycles form a local V2V mesh that disseminates compact hazard digests using latency-bounded gossip with adaptive fanout, TTL-based suppression, and redundancy-aware forwarding over sidelink-capable V2X links. The hazard channel is formulated as uncertainty-aware to account for localization error and propagation delay at race pace. We evaluate the system in two stages: (i) a reproducible mobility-coupled simulation/emulation campaign for mesh dissemination and durable edge → gateway → cloud delivery; and (ii) an MDF4 replay-based Jerez pilot for stability-oriented co-design analysis. Under the tested conditions, the durable MQTT path achieved an 83.4 ms median, 175.9 ms p95, and 303.74 ms maximum end-to-end latency with no observed event loss. In the Jerez pilot, the co-design workflow reduced mean wheel slip from 6.26% to 3.75% (−40.10%) and a control-volatility proxy from 0.1290 to 0.0212 (−83.58%).

Telecom doi: 10.3390/telecom7020046

Authors: Mohamed M. Gad Mai O. Sallam Allam M. Ameen Mohamed H. Bakr Ezzeldin A. Soliman

A compact and low-profile S-slot antenna for millimeter-wave wireless communication applications is presented in this paper. The antenna employs an S-shaped slot etched within a ground plane and excited by a hook-shaped microstrip feeding line to radiate a linearly polarized wave with a bidirectional broadside radiation beam. The antenna geometrical parameters are optimized to cover the n257 and n261 5G bands of the 5G mobile communications. The proposed antenna is fabricated and measured. Simulated and measured results demonstrate good impedance matching, with a measured fractional bandwidth of 18.3% and a maximum realized gain of 4.8 dBi across the desired operating bandwidth for the S-slot antenna with extended ground plane necessary for the purpose of measurements. The performance remains largely unaffected when the ground plane is reduced, highlighting the antenna’s suitability for compact implementations. Consequently, the proposed antenna is well suited for indoor 5G small-cell deployments and future railway wireless communication systems. Moreover, it can serve as a unit element in MIMO arrays or larger antenna configurations. To further demonstrate scalability and system-level applicability, the antenna element is extended into a compact eight-element MIMO array providing dual linear polarization. The array exhibits low mutual coupling, an envelope correlation coefficient on the order of 10−3, and a diversity gain approaching 10 dB. These results demonstrate highly independent radiation characteristics and reliable MIMO performance in multipath environments.

Telecom doi: 10.3390/telecom7020045

Authors: Zhihua Li Xinpei Xu Jintao Wang Mingyang Si Zhongcheng Wei

A low-overhead joint synchronization and channel estimation method for conventional CP-OFDM systems is developed to mitigate the error accumulation of stage-wise processing under multipath fading and carrier frequency offset (CFO). The joint estimation of symbol timing offset (STO), CFO, and channel parameters is formulated in a least-squares framework, and the analytical elimination of the channel vector reduces the original three-dimensional optimization to a two-dimensional search. In addition, reusable common terms and a precomputable pseudoinverse-related operator are exploited to reduce redundant online computations. Simulation results show that, under different signal-to-noise ratio (SNR) and normalized CFO conditions, the method achieves higher perfect synchronization probability and lower root-mean-square error (RMSE) for STO, CFO, and channel estimation than conventional CP-based baselines, while providing a favorable trade-off between estimation accuracy and computational complexity.

Telecom doi: 10.3390/telecom7020044

Authors: AN Soumana Hamadou Shengzhi Du Thomas O. Olwal Barend J. Van Wyk

The next generations of wireless networks will use more intensively shared spectrum and hardware resources. This leads to huge demand for integrated sensing and communication (ISAC) technology. Additionally, the integration of millimeter-wave (mmWave) spectrum can improve the sensing capabilities and communication rates of ISAC systems. This development is of great significance to the internet of things (IoT), as it is essential for intelligent operations and decision-making to have accurate surround sensing and device communication. This study presents a novel methodology for beamforming design in mmWave ISAC base stations within IoT systems, utilizing a grey wolf optimizer (GWO) to optimize the total communication rate and effective sensing power. Also, this work is mostly focused on simulation and heuristic optimization methods. The analyses conducted indicate that the suggested GWO-based optimization achieves a sum rate of up to 22.7 bit/s/Hz and a sensing power of 65.8 dBm when the base station (BS) is equipped with 8 antennas, in comparison to the results from the particle swarm optimization (PSO)-based and genetic algorithm (GA)-based schemes.

Telecom doi: 10.3390/telecom7020043

Authors: Natesh Kumar Mariano Falcitelli Francesco Kotopulos De Angelis Paolo Pagano Sandro Noto

The rapid growth of Internet of Things (IoT) deployments in hybrid terrestrial/non-terrestrial networks (TN/NTN) faces a major bottleneck: the verbosity of standard data formats like JSON. This is critical for large-scale M2M systems tracking and monitoring multimodal dry containers, where devices must comply with the strict message-size limits of commercial satellite IoT (around 160 bytes per message). We present a comparative evaluation of four device-friendly binary serialization protocols (CBOR, MessagePack, Protocol Buffers, and a custom Struct+Zlib hybrid) targeted at battery-powered microcontrollers. Using a horizontally scalable testbed with up to 2000 concurrent devices and the oneM2M standard framework, we assess payload efficiency, throughput, latency, and maintainability. Only Protocol Buffers and Struct+Zlib meet NTN message-size limits, with Protocol Buffers providing the best trade-off between performance and long-term maintainability. Real-world validation with the Astrocast LEO satellite platform and the oneM2M Mobius framework confirms these results. Cost analysis suggests potential savings exceeding €62,000 per month for a 10,000-device maritime fleet, demonstrating both technical feasibility and economic viability. This study provides a methodological framework for designing efficient, scalable IoT systems in hybrid TN/NTN networks, offering practical guidance for global container tracking and monitoring deployments.

Telecom doi: 10.3390/telecom7020042

Authors: Przemysław Falkowski-Gilski

We live in a digital society filled with cutting-edge ICT solutions [...]

Telecom doi: 10.3390/telecom7020041

Authors: Muhammad Sheraz Teong Chee Chuah It Ee Lee

Integrated sensing and communications (ISAC) requires tight coordination between spatial signal design and multiple-access strategies to balance communication throughput and sensing accuracy under shared spectral and hardware constraints. However, existing ISAC frameworks with rate-splitting multiple access (RSMA) typically rely on fixed antenna arrays and decoupled optimization, which fundamentally limit their ability to adapt to fast channel variations and dynamic sensing requirements. This paper introduces a fluid antenna-enabled RSMA-assisted ISAC architecture, in which movable antenna ports are exploited as a new spatial degree of freedom to enhance adaptability in both communication and sensing operations. Fluid antenna systems (FAS) are deployed at both the base station and user terminals, allowing dynamic port selection that reshapes the effective channel and sensing beampattern in real time. We formulate a joint sum-rate maximization problem subject to explicit sensing-quality constraints, capturing the coupled impact of antenna port selection, RSMA rate allocation, and multi-beam transmit design. The proposed framework maximizes the communication sum-rate while ensuring that the sensing functionality satisfies a predefined sensing quality constraint. This constraint-based ISAC formulation guarantees that sufficient sensing power is directed toward the target while optimizing communication performance. The resulting optimization involves strongly coupled discrete and continuous decision variables, rendering conventional optimization methods ineffective. To address this challenge, a hierarchical deep reinforcement learning (HDRL) framework is developed, where an upper-layer deep Q-network (DQN) determines discrete antenna port selection and a lower-layer twin delayed deep deterministic policy gradient (TD3) algorithm optimizes continuous beamforming and rate-splitting parameters. Numerical results demonstrate that the proposed approach significantly improves system performance, achieving higher communication sum-rate while satisfying sensing requirements under dynamic propagation conditions.

Telecom doi: 10.3390/telecom7020040

Authors: Mohammed Al Saleh Rima Shbaro Joseph Azar

This paper aims to secure sensitive data generated by IoT devices by introducing a lightweight hybrid approach that combines steganography and cryptography. While classical cryptography offers confidentiality guarantees, the visibility of the produced ciphertexts keeps them at risk of traffic analysis, which could reveal communication patterns. Although some studies use Curve25519-based protocols, ECC paired with RDWT, or VLSB-based steganography, there is no complete approach that combines cryptographic and steganographic methods that is tailored to IoT devices. Our proposed scheme addresses this gap by integrating X25519 with Elligator 2 for efficient key exchange, using Ascon-AEAD128 for encryption, and finally hiding the encrypted payload within cover images using hybrid DWT-DCT steganography. When compared to similar hybrid approaches, our method achieves better performance, with results showing high imperceptibility, low computational overhead, and good resistance to noise. The cryptographic-steganographic combo adopted by our proposed framework improves confidentiality, integrity, and resistance to detection in resource-constrained IoT systems.

Telecom doi: 10.3390/telecom7020039

Authors: Aleksandrs Ribalko Elans Grabs Aleksandrs Madijarovs Armands Lahs Toms Karklins Anna Karklina Aleksandrs Romanovs Ernests Petersons Lilita Gegere Aleksandrs Ipatovs

This paper investigates the quality of mobile network coverage along the Riga–Tukums railway corridor with a focus on the performance of 4G and 5G technologies. Ensuring reliable mobile connectivity along suburban railway corridors remains a significant technical challenge due to mixed forest–urban propagation conditions, macro-cell-dominated LTE infrastructure, mobility-induced channel variability, and fluctuating passenger density. Unlike high-speed railway environments that are extensively studied in dedicated 5G-R scenarios, suburban railway systems often rely on existing macro-cell deployments, where coverage continuity, signal quality stability, and capacity constraints must be addressed simultaneously. This study presents a measurement-based evaluation of 4G and 5G radio performance along the Riga–Tukums railway corridor under real operational conditions (50–90 km/h). Classical propagation models (Okumura–Hata and COST231-Hata) are quantitatively validated using MAE and RMSE metrics, followed by correlation analysis between RSSNR and QoS indicators. A theoretical Doppler sensitivity assessment (80–200 km/h) is conducted to evaluate mobility robustness across LTE and 5G frequency bands. Mobility transition regions and handover-related time windows are geometrically estimated, and passenger density-based capacity modeling is applied to assess throughput degradation under peak occupancy scenarios. Based on these results, a multi-layer network planning strategy integrating 700 MHz macro coverage, 1700 MHz capacity enhancement, and 3500 MHz 5G NR deployment is proposed. The optimization strategy resulted in an estimated 22–28% increase in stable service coverage in previously weak-signal zones and demonstrated that propagation model deviations remain within ranges comparable to recent railway studies (≈15–25 dB RMSE). These findings provide a structured framework for suburban railway communication optimization and support the gradual modernization of railway infrastructure toward FRMCS-ready architectures. The study illustrates the applicability of modern modelling tools for assessing and improving mobile communication systems and contributes to the broader development of digital infrastructure within Latvia’s transport sector.

Telecom doi: 10.3390/telecom7020038

Authors: Samuel Otero Rebolo Victor Monzon Baeza

The evolution toward prospective sixth-generation (6G) wireless networks is expected to significantly increase user density, bandwidth demand, and architectural complexity, reinforcing the need for scalable multiple-input multiple-output (MIMO) deployments. In this context, two fundamentally different design strategies have emerged: scaling up centralized antenna arrays and scaling out distributed cooperative infrastructures. This paper presents a system-level comparative benchmark of scale-up and scale-out MIMO architectures under identical operating conditions of three representative downlink deployments: centralized Massive MIMO, centralized XL-Massive MIMO, and distributed Cell-Free MIMO. All architectures are assessed under identical urban channel conditions, transmit power, bandwidth, and traffic assumptions, considering sub-6 GHz (3.5 GHz) and millimeter-wave (28 GHz) frequency bands as proxies for 5G and prospective 6G operation. A unified Monte Carlo simulation framework is employed to jointly evaluate aggregate throughput, spectral efficiency, coverage performance, interference behavior, and energy efficiency over a wide range of user densities and service radii. The results highlight the distinct architectural trade-offs between centralized and distributed deployments: XL-Massive MIMO maximizes aggregate throughput and spatial reuse in dense hotspot scenarios, whereas Cell-Free MIMO provides superior coverage uniformity and improved energy efficiency in wide-area deployments. By isolating the impact of architectural scaling under consistent assumptions, the presented benchmark offers quantitative guidance for 6G network design and deployment planning.

Telecom doi: 10.3390/telecom7020037

Authors: Parichehr Dadkhah Parvin Rastegari Mohammad Dakhilalian Phil Yeoh Mingzhong Wang Shahrzad Saremi Rania Shibl Yassine Himeur Wathiq Mansoor

Healthcare Wireless Sensor Networks (HWSNs) have attracted significant attention due to their vital role in diseases’ diagnosis, monitoring, and treatment. By continuously collecting patients’ physiological data and enabling remote medical services, these networks can greatly improve the quality of healthcare. However, the inadequate handling of security and privacy issues poses serious risks to patients. In this context, signcryption schemes are essential cryptographic primitives that simultaneously provide authentication, confidentiality, and data integrity with a low overhead. Recently, Deng et al. proposed a certificateless signcryption (CL-SC) scheme for HWSNs and proved its security in the standard model. In this paper, we demonstrate that their scheme is insecure under an enhanced adversarial model, where a super Type II adversary, which is a malicious key generation center, can replace the system’s master public key using the master secret key under its control, and subsequently forge valid signcryptions on arbitrary messages on behalf of a sensor node. To address this vulnerability, we propose an enhanced CL-SC scheme based on elliptic curve cryptography (ECC). Under the hardness assumptions of the Elliptic Curve Decisional Diffie–Hellman Problem (ECDDHP) and the Computation Attack Algorithm (CAA), the proposed scheme achieves confidentiality and existential unforgeability against both super Type I and super Type II adversaries in the standard model. Performance analysis further shows that our scheme is efficient and well suited for resource-constrained HWSN environments.

Telecom doi: 10.3390/telecom7020036

Authors: Anastasia V. Ermakova Oleg V. Varlamov

As mobile networks evolve toward Beyond 5G and 6G architectures, energy efficiency and sustainability have become increasingly critical due to growing traffic volumes, denser base station deployments, and the rising number of connected devices. Supporting Ultra-Reliable Low-Latency Communication (URLLC) services is particularly challenging, as their stringent requirements for both high reliability and minimal latency can lead to a significant increase in energy consumption within the radio access network. This paper examines slot structure mechanisms for concurrently servicing URLLC and enhanced Mobile Broadband (eMBB) traffic within the 5G Advanced framework, with a focus on improving energy efficiency and optimizing radio resource utilization. We propose an adaptive algorithm for managing radio interface time resources, which dynamically allocates sub-slots based on current network load and radio channel conditions. The system model is implemented in Simulink and incorporates URLLC and eMBB traffic generation, signal-to-noise ratio estimation, and a priority-based scheduling mechanism. Simulation results demonstrate that the proposed approach meets URLLC latency and reliability requirements while reducing redundant transmissions and enhancing the energy efficiency of the radio access network. These findings position the proposed method as a promising solution for the design of energy-efficient, next-generation mobile networks.

Telecom doi: 10.3390/telecom7020035

Authors: Christos Betzelos Dimitrios Uzunidis Anastasios Vetsos Panagiotis A. Karkazis

This paper advances user-centric Artificial Intelligence (AI) frameworks for reliability in fifth-generation and beyond (B5G) networks by examining their use in high-demand services such as video streaming. The proposed framework can leverage multi-layer monitoring across the edge–cloud continuum, application-layer metrics, and 5G core performance data to evaluate reliability through Quality of Experience (QoE) optimization. Results demonstrate that improved frame delivery can be achieved via dynamic resource prediction and proactive resource allocation. The study validates the framework’s scalability in dynamic workload conditions, emphasizing its role in mission-critical video services.

Telecom doi: 10.3390/telecom7020034

Authors: Cătălin-Eugen Bucur Georgiana Crihan Anamaria Rădoi Elena-Grațiela Robe-Voinea Iustin-Nicolae Moroșan

In the contemporary digital landscape, AI (Artificial Intelligence) emerged as a pivotal tool in enhancing the defense technologies developed across the entire network infrastructure. As reliance on AI-based decision-making grew, so did the imperative need for interpretability, transparency, and trustworthiness, leading to the development and integration of XAI (eXplainable Artificial Intelligence). This research paper provides a comprehensive overview of the current state of the art in XAI approaches that can be effectively implemented for network traffic monitoring, especially in critical digital infrastructures. The main contribution of this research article consists of the comparative analysis of the XAI SHAP (Shapley Additive Explanation) method applied to different datasets obtained from real-time network traffic monitoring, utilizing several representative parameters, which demonstrates the performance, vulnerabilities, and limitations of the proposed method, and also the security implications of the system resources from a cybersecurity perspective. Experimental results show that Ethernet networks offer higher predictability and clearer decision boundaries. Consequently, they are a safer solution for deployment in sensitive network architectures. In contrast, BYOD (Bring Your Own Device) Wi-Fi environments exhibit greater randomness.

Telecom doi: 10.3390/telecom7020033

Authors: Larry Wigger Anthony Vatterott

COVID-19 supply-chain disruptions clearly illustrated deficiencies in central coordination. Meaningful improvement in the central coordination of supply-chains will require transparency into resource stocks and flows. The latest technology, like 5G, blockchain and IoT, are primed to provide this transparency for collaboration during crises. This will improve agility and service, reduce inventory and enable reverse logistics benefits. Furthermore, transparent global networks can allow a more inclusive and equitable distribution of critical supply, yielding quicker resolution during crises. However, many challenges exist that suggest further delay in the adoption of a holistic and transparent digitalized supply chain. This paper explores the most recent pandemic with attention to the limiting factors at all levels of emergent global crisis response.

Telecom doi: 10.3390/telecom7020032

Authors: Palak Girdhar Muslem Al-Saidi Prashant Johri Deepali Virmani Hussein Taha Oday Ali Hassen

Human Activity Recognition (HAR) is a pivotal research area for applications such as automated surveillance, smart homes, security, healthcare, and human behavior analysis. Traditional machine-learning approaches often rely on manual feature engineering, which can limit generalization. Although deep learning has improved HAR through automatic representation learning, achieving high detection performance under computational constraints remains challenging. This paper proposes an efficient HAR framework that combines deep learning with hybrid optimization. Surveillance videos are first decomposed into frames, and a keyframe selection stage identifies distinctive frames to reduce redundancy and computational cost while preserving informative content. Motion and appearance features are then extracted using Histogram of Oriented Optical Flow (HOOF) and a ResNet-101 model, respectively, and concatenated into a unified feature representation. Classification is performed using an Inception-based Long Short-Term Memory (Incept-LSTM) network, which is fine-tuned via the proposed Tricky Predator Optimization (TricP) over a restricted, low-dimensional parameter vector. TricP is inspired by predator poaching behavior and the social dynamics of Latrans to enhance exploration and exploitation during search. Experiments on the UCF-Crime dataset show that the proposed method achieves 96.84% specificity, 92.16% sensitivity, and 93.62% accuracy.

Telecom doi: 10.3390/telecom7020031

Authors: Harishik Dev Singh Jamwal Saurabh Singh

Autonomous platforms are critical for accelerating disaster response by delivering situational awareness and search-and-rescue support without exposing human operators to risk. However, practitioners face significant challenges in selecting and implementing robust software on vendor-constrained, immutable hardware. This paper provides a comprehensive survey contrasting the capabilities of two complementary unmanned platforms: Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs). We analyze state-of-the-art software blueprints for perception, navigation, and coordination under the constraints of fixed hardware. Key contributions include a comparative analysis of mission suitability, a synthesis of emerging machine learning algorithms for robust navigation, and an identification of critical research gaps. While recent works have advanced specific algorithms, a comprehensive survey comparing software-driven approaches on fixed-hardware UAVs and UGVs is lacking, a gap this paper aims to fill. Our analysis reveals that the sim-to-real transfer gap, the absence of standardised disaster benchmarks, and limited explainability of deep-reinforcement-learning policies remain the most critical barriers to field deployment. We conclude with a prioritised research roadmap that groups open challenges into short-term (1–2 year) and long-term (3–5+ year) directions.

Telecom doi: 10.3390/telecom7020030

Authors: Mario E. Rivero-Ángeles Izlian. Y. Orea-Flores Iclia Villordo Jiménez Yesenia E. Gonzalez-Navarro

In classic communication systems, signals and data were mostly continuous in time, such as voice (fixed and mobile telephony, and radio systems) and video signals (Television services), Conversely, in modern communication systems, most signals are packet-based (text and images in messaging services and social media) and even continuous-time data has to be converted into a discrete-time nature data, such as video and voice services that are now discretized to be sent in packet-based communication systems. However, these classic communication systems were analyzed, studied, and designed using continuous-time analysis, such as the classic Erlang-B formula. This classic analysis can still be used in modern systems, but a discrete-based framework provides a seamless analysis and yields more accurate results. In this work, the effect of the system’s elementary time step is analyzed, and guidelines for its selection are provided to adequately analyze continuous-time systems within a discrete-time framework. To demonstrate the utility of the discretization and to consider these guidelines, we developed a mathematical analysis based on a discrete-time Markov chain to study a system with a buffer capacity under conventional and bursty traffic, which is commonly found in an Internet of Things application. The derived formulas allow us to quantify system performance under a discrete framework. This, in turn, allows us to provide some relevant guidelines for the elementary time step selection to adequately analyze continuous-time systems under a discrete-time framework.

Telecom doi: 10.3390/telecom7020029

Authors: Tomoya Kawana Rei Nakagawa Nariyoshi Yamai

When devices equipped with multiple wireless network interfaces access the Internet via Wi-Fi, 4G, and 5G, external factors such as radio interference can increase packet loss rates, resulting in reduced communication speed. To address this issue, two approaches exist: the use of Bottleneck Bandwidth and Round-trip propagation time (BBR), a congestion control algorithm designed to mitigate the impact of packet loss and bicasting in multihomed networks. Bicasting in multihomed networks exploits multiple network paths by transmitting identical packets simultaneously over different networks, thereby reducing effective packet loss and mitigating throughput reduction. In this paper, we introduce a novel network architecture that effectively operates in lossy networks by combining bicasting with BBR. By utilizing QUIC and OpenFlow, the proposed architecture enables the construction of a multihomed network that is independent of the operating system (OS), allowing flexible configuration of congestion control algorithms. Furthermore, the introduction of a QUIC proxy enables the use of existing server-side applications without requiring any modifications. Using the proposed multihomed network, we evaluate communication performance for unicasting and bicasting under varying packet loss rates, and we also analyze fairness with competing Transmission control protocol (TCP) flows. The results indicate that the combination of BBRv3 and bicasting achieves fivefold higher throughput than TCP unicasting at a 1% packet loss rate while preserving fairness with competing TCP flows.

Telecom doi: 10.3390/telecom7020028

Authors: Shenghan Luo Fangxiao Li Leyi Shi Dawei Zhao

As network attack methods continue to evolve, flooding attacks remain a major threat that causes network paralysis and service disruption. Statically configured systems are particularly vulnerable, as attackers can exploit reconnaissance information to launch large-scale attacks, while conventional defense mechanisms often fail under high-intensity traffic. To address this problem, this paper introduces Moving Target Defense (MTD) within a decentralized framework and proposes a blockchain-based decentralized End Hopping system. The system employs the Practical Byzantine Fault Tolerance (PBFT) consensus protocol for dynamic controller election and incorporates a disaster recovery mechanism, which eliminates single points of failure while ensuring reliable controller transitions and rapid service restoration. Experimental results demonstrate that the proposed system achieves satisfactory performance in terms of availability, effectiveness, and security, providing a practical approach to constructing robust proactive defense networks.