Telecom

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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 $2500\mathrm{m}\times 2500\mathrm{m}$ 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 $10\times 10$, $20\times 20$, and $30\times 30$ 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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article