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.

Telecom doi: 10.3390/telecom7020027

Authors: Lin Shi Chuansheng Yan Dingcheng Yang Yu Xu Fahui Wu Huabing Lu

We propose a unmanned aerial vehicle (UAV)-assisted integrated sensing, communication, and jamming (U-ISJC) framework, in which a multifunctional UAV first detects the sensing target to obtain sensing information, and subsequently transmits the information to communication users via a unified beam in the presence of multiple eavesdroppers. To avoid functional conflicts, a time slot frame structure is designed for the UAV’s multifunctional capabilities, enabling communication, sensing, and jamming tasks within each timeslot. The time slot allocation factor dynamically adjusts based on the UAV’s flight trajectory for efficient UAV resource utilization. Additionally, to prevent security rate leakage caused by eavesdroppers, a jamming beam is added to serve both jamming and sensing functions. Our objective is to maximize the the worst-case total secure data transmission rate by jointly optimizing sub-time slot allocation, beamforming, and UAV trajectory. To address this problem, we propose a joint optimization algorithm that adopts the concave–convex procedure (CCCP) technique and semi-definite relaxation (SDR), under the block coordinate descent (BCD) framework. The simulation results show that compared with the baseline scheme, the proposed algorithm substantially improves the communication security rate while ensuring the quality of communication and sensing.

Telecom doi: 10.3390/telecom7020026

Authors: Wei Pang Ying Zhang

Reconfigurable intelligent surfaces (RISs) hold promising technical prospects for 6G wireless communications to enhance system capacity, coverage and sum-rate. Unlike existing studies deploying only passive or active RISs, this paper adopts a novel hybrid RIS architecture that optimally allocates the number of active and passive elements. Under fixed quantities of both RIS element types in the fixed hybrid RIS, it simultaneously increases the number of base station antennas and served users, focusing on solving rate optimization for hybrid RIS-assisted MISO systems deployed in various scenarios. This paper establishes a fundamental model for hybrid RIS reflection signals. To better characterize the performance of the proposed hybrid RIS architecture, an optimization problem is formulated to maximize the sum-rate of the hybrid RIS-assisted multi-user, multiple-input, single-output (MU-MISO) system. An efficient algorithm is proposed combining fractional programming (FP), alternating optimization, and Lagrange duality transformation. Simulation results demonstrate that with hybrid RIS assistance, the system’s sum-rate gain increases by 49.1% and 40%, respectively, compared to systems with only active RIS deployment. This achieves higher sum-rate gains at lower power consumption.

Telecom doi: 10.3390/telecom7020025

Authors: Zhipeng Wang Jin Li Shuai Zhang Dechuan Chen

To address the rigorous requirements of ultra-reliable low-latency communication (URLLC) in beyond 5G/6G networks, we propose an innovative architecture combining automatic repeat request (ARQ) protocol with a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) to enhance short-packet non-orthogonal multiple access (NOMA) communications. Specifically, retransmission mechanism provided by ARQ is utilized to mitigate packet errors stemming from practical system imperfections, i.e., imperfect channel state information (ipCSI), imperfect successive interference cancellation (ipSIC), and hardware impairments. Using the analytical foundation provided by finite blocklength (FBL) theory, expressions for two key performance metrics, i.e., the average block error rate (BLER) and effective throughput, are derived for two NOMA users. Simulation results validate the analytical derivations and demonstrate that the ARQ scheme provides significant reliability gains for each user and achieves synergistic gain with STAR-RIS technology. In addition, the effective throughput exhibits a peak at an optimal blocklength, balancing the reliability gain from a longer blocklength against the spectral efficiency loss from a lower coding rate. This optimal blocklength decreases with more STAR-RIS elements, as improved channel conditions reduce the need for long blocklengths.

Telecom doi: 10.3390/telecom7020024

Authors: Francesco Nucci Gabriele Papadia

The proliferation of Internet of Things (IoT) devices demands efficient resource management in fifth-generation (5G) networks, particularly through network slicing mechanisms supporting massive machine-type communications (mMTCs). This paper addresses IoT connectivity in 5G network slicing through a bi-objective optimization framework balancing operational costs with quality-of-service. We formulate a bi-objective optimization problem that balances operational costs with quality-of-service (QoS) requirements across heterogeneous 5G network slices. The proposed approach employs a tailored Non-dominated Sorting Genetic Algorithm II (NSGA-II) incorporating domain-specific constraints, including device priorities, slicing isolation requirements, radio resource limitations, and battery capacity. Through extensive simulations on scenarios with up to 5000 devices, our method generates diverse Pareto-optimal solutions achieving hypervolume improvements of 8–13% over multi-objective DRL, 15–28% over single-objective DRL baselines, and 22–41% over heuristic approaches while maintaining computational scalability suitable for real-time network management (sub-2 min execution). Validation with real-world traffic traces from operational deployments confirms algorithm robustness under realistic burstiness and temporal patterns, with 7% performance degradation vs. synthetic traffic—within expected simulation–reality gaps. This work provides a practical framework for IoT resource scheduling in current 5G and future Beyond-5G (B5G) telecommunications infrastructures, validated in scenarios of up to 5000 devices.

Telecom doi: 10.3390/telecom7010023

Authors: Paul Scalise Michael Hempel Hamid Sharif

5G systems have delivered on their promise of seamless connectivity and efficiency improvements since their global rollout began in 2020. However, maintaining subscriber identity privacy on the network remains a critical challenge. The 3GPP specifications define numerous identifiers associated with the subscriber and their activity, all of which are critical to the operations of cellular networks. While the introduction of the Subscription Concealed Identifier (SUCI) protects users across the air interface, the 5G Core Network (CN) continues to operate largely on the basis of the Subscription Permanent Identifier (SUPI)—the 5G-equivalent to the IMSI from prior generations—for functions such as authentication, billing, session management, emergency services, and lawful interception. Furthermore, the SUPI relies solely on the transport layer’s encryption for protection from malicious observation and tracking of the SUPI across activities. The crucial role of the largely unprotected SUPI and other closely related identifiers creates a high-value target for insider threats, malware campaigns, and data exfiltration, effectively rendering the Mobile Network Operator (MNO) a single point of failure for identity privacy. In this paper, we analyze the architectural vulnerabilities of identity persistence within the CN, challenging the legacy “honest-but-curious” trust model. To quantify the extent of subscriber identities being utilized and exchange within various API calls in the CN, we conducted a study of the occurrence of SUPI as a parameter throughout the collection of 5G SBI (Service-Based Interface) Core VNF (Virtual Network Function) API (Application Programming Interface) schemas. Our extensive analysis of the 3GPP specifications for 3GPP Release 18 revealed a total of 4284 distinct parameter names being used across all API calls, with a total of 171,466 occurrences across the API schema. More importantly, it revealed a highly skewed distribution in which subscriber identity plays a pivotal role. Specifically, the “supi” parameter ranks 57th with 397 occurrences. We found that SUPI occurs both as a direct parameter (“supi”) and within 72 other parameter names that contain subscriber identifiers as defined in 3GPP TS 23.003. For these 73 parameter names, we identified a total of 8757 occurrences. At over 5.11% of all parameter occurrences, this constitutes a disproportionately large share of total references. We also detail scenarios where subscriber privacy can be compromised by internal actors and review future privacy-preserving frameworks that aim to decouple subscriber identity from network operations. By suggesting a shift towards a zero-trust model for CN architecture and providing subscribers with greater control over their identity management, this work also offers a potential roadmap for mitigating insider threats in current deployments and influencing specific standardization and regulatory requirements for future 6G and Beyond-6G networks.

Telecom doi: 10.3390/telecom7010022

Authors: Ivan Nedyalkov

This paper presents a methodology for studying the level of network security of VoIP platforms. The methodology is designed for VoIP platforms where the voice and video traffic passes through and are processed by the VoIP server itself, rather than being exchanged directly between the end devices. The proposed methodology consists of four stages: scanning for open ports; scanning for well-known vulnerabilities; penetration testing; and finally, analysis and recommendations (if necessary). Well-known tools used for monitoring IP networks were used to implement the methodology: Namp, Wireshark, hping3, and Colasoft Capsa Free. The studied VoIP platforms were VitalPBX and Issabel, which are based on the Asterisk FreePBX platform. The penetration tests included attacking VitalPBX and Issabel with TCP and UDP DoS attacks. The penetration tests were carried out and implemented using the GNS3 IP network modeling platform. This study found that Issabel has many more unnecessarily open ports than VitalPBX; on both platforms, DoS attacks are likely to be unsuccessful, which was confirmed by the experimental studies carried out. The applicability of the proposed methodology was confirmed by the study carried out.

Telecom doi: 10.3390/telecom7010021

Authors: Quadri R. Adebowale Shawn Ostermann

Future lunar missions require robust 5G communication links, and their design depends partly on path loss characterization, link budget planning inputs, and path prediction loss models tailored to the Moon’s environmental conditions. This work develops a site-specific 5G large-scale path loss model for Shoemaker Rim F at 5.855 GHz using a high-resolution lunar digital elevation map and 3D ray tracing in Wireless Insite. Two link configurations were studied—dipole transmitter to dipole receiver (DD) and omni transmitter to dipole receiver (OD)—under five path loss cases: measured path loss, free space path loss (FSPL) with and without antenna patterns, and excess path loss with and without antenna patterns. The close-in (CI) and floating intercept (FI) model parameters are derived to develop a mathematical model for path loss prediction for the Shoemaker RIF’s terrain on the lunar south pole. The CI and FI for the DD configuration revealed a path loss exponent of 2.5378 and RMSE values of 45.15 dB and 43.898 dB, while the CI and FI for the OD configuration yielded a path loss exponent of 4.3280 and RMSE values of 6.301 dB and 66.739 dB, indicating strong sensitivity to the transmitter radiation pattern.

Telecom doi: 10.3390/telecom7010020

Authors: Akeem Abimbola Raji Thomas Otieno Olwal

The proliferation of data-intensive IoT applications has created unprecedented demand for wireless spectrum, necessitating more efficient bandwidth management. Spectrum sensing allows unlicensed secondary users to dynamically access idle channels assigned to primary users. However, traditional sensing techniques are hindered by their sensitivity to noise and reliance on prior knowledge of primary user signals. This limitation has propelled research into machine learning (ML) and deep learning (DL) solutions, which operate without such constraints. This study presents a comprehensive performance assessment of prominent ML models: random forest (RF), K-nearest neighbor (KNN), and support vector machine (SVM) against DL architectures, namely a convolutional neural network (CNN) and an Autoencoder. Evaluated using a robust suite of metrics (probability of detection, false alarm, missed detection, accuracy, and F1-score), the results reveal the clear and consistent superiority of RF. Notably, RF achieved a probability of detection of 95.7%, accuracy of 97.17%, and an F1-score of 96.93%, while maintaining excellent performance in low signal-to-noise ratio (SNR) conditions, even surpassing existing hybrid DL models. These findings underscore RF’s exceptional noise resilience and establish it as an ideal, high-performance candidate for practical spectrum sensing in wireless networks.

Telecom doi: 10.3390/telecom7010019

Authors: Louiza Hamada Pascal Lorenz

This article discusses the fundamental limitations of Light Fidelity (Li-Fi) systems, an emerging visible light communication technology that is constrained by line-of-sight dependency and optical attenuation. Unlike existing adaptive modulation approaches that focus solely on improving signal processing, we present an integrated framework that combines three key contributions: (1) an adaptive modulation optimization algorithm that selects among OOK, PAM, and OFDM schemes based on instantaneous signal-to-noise ratio thresholds, achieving a 30–40% range extension compared to fixed modulation references; (2) a method for spatial optimization of access points (APs) using the L-BFGS-B algorithm to determine the optimal location of APs, taking into account lighting constraints and coverage uniformity; and (3) comprehensive system-level modeling incorporating shot noise, thermal noise, inter-symbol interference, and dynamic shadowing effects for realistic performance evaluation. Through extensive simulations on multiple room geometries (6 m × 5 m to 20 m × 15 m) and AP configurations (one to six APs), we demonstrate that the proposed adaptive system achieves an average throughput 60% higher than that of fixed OOK, while maintaining 98.7% coverage in a 10 m × 8 m environment with two optimally placed APs. The framework provides practical design guidelines for Li-Fi deployment, including an analysis of computational complexity O(M×N) for coverage assessment, O(I×D3) for access point optimization) and a characterization of convergence behavior. A comparative analysis with state-of-the-art techniques (optical smart reflective surfaces, machine learning-based blockage prediction, and Li-Fi/RF hybrid configurations) positions our lightweight algorithmic approach as suitable for resource-constrained deployment scenarios, where system-level integration and practical feasibility take precedence over innovation in individual components.

Telecom doi: 10.3390/telecom7010018

Authors: Antonio Francesco Gentile Maria Cilione

Modern TCP/IP networks are increasingly exposed to unpredictable conditions, both from the physical transmission medium and from malicious cyber threats. Traditional stochastic models often fail to capture the non-linear and highly sensitive nature of these disturbances. This work introduces a formal mathematical framework combining classical network modeling with chaos theory to describe perturbations in latency and packet loss, alongside adversarial processes such as denial-of-service, packet injection, or routing attacks. By structuring the problem into four scenarios (quiescent, perturbed, attacked, perturbed-attacked), the model enables a systematic exploration of resilience and emergent dynamics. The integration of artificial intelligence techniques further enhances this approach, allowing automated detection of chaotic patterns, anomaly classification, and predictive analytics. Machine learning models trained on simulation outputs can identify subtle signatures distinguishing chaotic perturbations from cyber attacks, supporting proactive defense and adaptive traffic engineering. This combination of formal modeling, chaos theory, and AI-driven analysis provides network engineers and security specialists with a powerful toolkit to understand, predict, and mitigate complex threats that go beyond conventional probabilistic assumptions. The result is a more robust methodology for safeguarding critical infrastructures in highly dynamic and adversarial environments.

Telecom doi: 10.3390/telecom7010017

Authors: Adolphe D. J. Nseme Larbi Talbi Vincent A. Fono

The increasing deployment of humanoid robots in indoor environments such as smart factories, laboratories, offices, and hospitals poses new challenges to millimeter-wave wireless communication systems. Existing human body obstruction models, while effective at characterizing pedestrian-induced signal attenuation, are not designed to directly capture the structural geometry, material composition, and controlled mobility of humanoid robotic platforms. In this work, we first reproduce a well-established human-body-based propagation model under comparable indoor conditions and subsequently extend this hybrid framework to controlled humanoid-based scenarios by combining double knife-edge diffraction (DKED) with a modified street-canyon reflection model operating at 28 GHz. Compared to existing human-based studies, the proposed approach explicitly incorporates the material properties of the humanoid robot’s envelope through a calibrated correction factor and accounts for its controlled lateral movements. An indoor measurement campaign using three programmable humanoid robots was conducted to evaluate the model. Experimental results show that humanoid robots can reproduce attenuation trends and obstruction dynamics consistent with those reported in prior human-body blockage studies, while offering improved repeatability and greater experimental control. The proposed framework provides a practical and reproducible tool for modeling indoor millimeter-wave channels under controlled humanoid-based experimental conditions, in environments involving mobile robotic agents.

Telecom doi: 10.3390/telecom7010016

Authors: Mohamed Guermal Jamal Zbitou Fouad Aytouna Stephane Ginestar Mohammed El Gibari

This paper presents the design of a highly reconfigurable interdigital bandpass filter (BPF) developed through a three-stage design approach. In the first stage, the influence of four low-loss dielectric substrates on the filter response is systematically analyzed to identify the optimal configuration. The selected substrate demonstrates excellent performance, achieving an input return loss of −38 dB, an insertion loss of −0.9 dB at 4.30 GHz, and a wide passband corresponding to a bandwidth (BW) of 2.20 GHz. In the second stage, two variable capacitors were incorporated into the baseline geometry, enabling manual tuning of the center frequency (f0) from 5.10 to 6.34 GHz, with (S11) better than −25 dB and (S12) close to −0.60 dB. In the final stage, the capacitors were replaced by SMV1413 varactor diodes, transforming the design into a fully voltage-controlled tunable filter. This configuration provides continuous frequency agility from 4.70 to 5 GHz without modifying the physical structure, while achieving (S11) levels down to −40 dB and insertion loss as low as −0.7 dB. The proposed architecture offers a compact, low-loss, and electrically reconfigurable solution, making it a promising solution for next-generation RF front-ends, adaptive wireless systems, and cognitive radio applications. Two independent Electromagnetic solvers (EM) were employed to validate the filter’s performance: an EM based on the Finite Integration Technique and the Advanced Design System 2026 (ADS) solver using the Method of Moments (MoM). The close agreement between the results produced by both platforms confirms the accuracy and robustness of the proposed reconfigurable bandpass filter structure.

Telecom doi: 10.3390/telecom7010015

Authors: Xiaoguang Hu Junpeng Cui Rui Zhang Quanrong Fang

With the evolution of Reconfigurable Intelligent Surface (RIS) technology, its potential for dynamically optimizing wireless channels has garnered significant attention. However, existing methods still face challenges in real-time control in complex environments due to high computational complexity. To address this, this paper proposes a reconfigurable wireless channel optimization framework based on Intelligent Metasurfaces 2.0 and designs a low-complexity control strategy. The strategy integrates an adaptive adjustment mechanism and multi-dimensional feedback, aiming to reduce system computational load. Experimental results show that compared to traditional methods (such as MRC and MMSE), the proposed method improves signal transmission quality (SNR improvement of 3.8 dB) and system stability (exponential increase to 0.92). When compared to advanced deep reinforcement learning (DRL) and graph neural network (GNN) methods, it achieves similar signal quality while reducing computational overhead by 20.0% and energy consumption by approximately 32.4%. Ablation experiments further verify the effectiveness and synergistic role of the proposed core modules. This study provides a feasible approach toward high-efficiency, low-complexity dynamic channel optimization in 5G and future communication networks.

Telecom doi: 10.3390/telecom7010014

Authors: Davor Vinko Mirko Köhler Kruno Miličević Ivica Lukić

Accredited laboratories participating in Proficiency Testing (PT) and Interlaboratory Comparison (ILC) typically submit measurement results (and associated uncertainties) to an organizer for performance evaluation using statistics such as the z-score and the En value. This requirement can undermine confidentiality when the disclosed plaintext values reveal commercially sensitive methods or client-related information. This paper proposes a secure-by-design PT/ILC workflow based on fully homomorphic encryption (FHE), enabling the required scoring computations to be executed directly on ciphertexts. Using the CKKS scheme (Microsoft SEAL), the organizer distributes encrypted assigned values and a public/evaluation key set; each participant locally encrypts pre-processed measurement data, evaluates encrypted z-score and En value, and returns only encrypted performance metrics. The organizer decrypts the metrics without receiving the ciphertexts of participants’ raw measurement values. We quantify feasibility via execution time, run-to-run variability across fresh key generations (coefficient of variation), and relative calculation error versus plaintext scoring. On commodity hardware, end-to-end score computation takes 1 to 8 s, the coefficient of variation can be reduced below 1e−10, and the relative error remains below 1e−6, indicating practical deployability and numerical stability for PT/ILC decision-making. Given that PT/ILC reporting cycles are typically on the order of days to weeks, a per-participant computation time of seconds is operationally negligible, while the observed coefficient of variation and relative error indicate that the CKKS approximation and key-dependent variability are far below typical decision thresholds used for pass/fail classification.

Telecom doi: 10.3390/telecom7010013

Authors: Jagdeep Singh Sanjay K. Dhurandher Isaac Woungang Petros Nicopolitidis

Intermittently Connected Wireless Networks (ICWNs) are characterized by dynamic node mobility and the absence of persistent end-to-end paths, making them highly susceptible to security threats. This paper proposes a novel secure routing protocol, called the Evolutionary Game Theoretic model with Reinforcement Learning (EGT-RL), designed to provide adaptive and resilient protection against blackhole attacks in such networks. EGT-RL integrates Q-learning for dynamic threat assessment with evolutionary game theory to model and influence node behavior over time. Simulation results, based on both synthetic and real-world mobility traces, show that EGT-RL significantly outperforms three benchmark protocols in delivery ratio, packet drops, end-to-end latency, and communication overhead.

Telecom doi: 10.3390/telecom7010012

Authors: Mario Eduardo Rivero-Ángeles Izlian Yolanda Orea-Flores

To this date, the Fifth Generation (5G) of mobile communications has been deployed and has opened a great number of opportunities by increasing transmission rates (partialy through the use of MIMO systems), decreasing latency, providing the amount of bandwidth required for video services, Virual and Augmented Reality applications, and social media and providing a solid ground for the massive implementation of the Internet of Things (IoT), which we believe is still in its initial phases of development [...]

Telecom doi: 10.3390/telecom7010011

Authors: Akinsanmi Akinbolati Bolanle T. Abe

The metric for probing the variation in atmospheric refractive indices is radio refractivity (RR), which is a key factor in determining the losses associated with a radio signal as it traverses from one atmospheric layer to another. Ten years (2015–2024) of surface hourly data of temperature (K), pressure (P), and relative humidity (RH) obtained from ERA-5 reanalysis were used for RR computations based on ITU-R models. Twelve major cities of South Africa were benchmarked for the study. Time series plots of the overall ten-year RR hourly mean were generated for the cities. The correlation coefficient (R) between RR and RH was investigated. The results indicate the highest and lowest RR of 360.94 and 301.09 (N-Units) in Pietermaritzburg and Kimberly, respectively, with a range of 59.85 over the country. In the southern coast, Pietermaritzburg recorded the highest and lowest values of 360.14 and 325.52 (N-Units) at 21:00 and 11:00 hrs., followed by Durban with 348.55 and 339.44 at 17:00 and 10:00 hrs., Bhisho with 346.88 and 320.622 at 00:00 and 11:00 hrs., and Cape Town with 328.54 and 322.47 (N-Units) at 00:00 and 10:00 hrs., respectively. In the central region, Bloemfontein recorded values of 344.97 and 305.58 at 04:00 and 13:00 hrs., respectively, while Kimberly recorded 338.06 and 301.09 at 04:00 and 13:00 hrs., respectively. In the northern region, Johannesburg recorded the highest and lowest values of 358.79 and 318.56 (N-Units) at 03:00 and 13:00 hrs., respectively; Pretoria recorded values of 352.25 and 316.76 at 04:00 and 13:00 hrs., respectively; Emalahleni recorded values of 358.79 and 318.95 at 03:00 and 13:00 hrs., respectively; and Polokwane recorded values of 357.59 and 320.82 at 03:00 and 13:00 hrs., respectively. Mahikeng recorded values of 346.70 and 311.37 at 04:00 and 13:00 h, while Mbombela recorded values of 360.11 and 329.17 (N-Units) at 00:00 and 12:00 h, respectively. The implications of these results are a higher refractive attenuation effect of terrestrial transmitted radio signals in cities with higher RR and during the early morning, evening, and night hours of the day. A high positive (R) of 0.84 to 0.99 was observed between RR and RH across the country. A geo-spatial RR contour map was generated for the study stations for practical applications and could be helpful in cities where the contour passes within South Africa. These findings should be taken into consideration in the design and reappraisal of terrestrial radio-link and power budgets to ensure quality of service. The overall findings provide practical applications for mitigating RR-prone attenuation on terrestrial radio channels, such as Radio and Television broadcasting, GSM, and microwave link systems, among others, across South Africa and other countries with similar geography and climate.

Telecom doi: 10.3390/telecom7010010

Authors: Madalin Neagu Codruta Maria Serban Anca Hangan Gheorghe Sebestyen

Resource-constrained Internet of Things (IoT) devices are increasingly deployed in critical domains but remain vulnerable to stealthy attacks that can bypass conventional defenses. At the same time, privacy constraints limit centralized data collection and processing, complicating anomaly detection. This systematic review surveys methods for privacy-preserving anomaly detection in resource-constrained IoT and introduces a five-dimension taxonomy covering deployment paradigms, resource constraints, real-time requirements, protection techniques, and communication constraints. We review how the literature measures and reports resource and privacy costs and identify three major gaps: (1) a shortage of co-designed detector-plus-privacy solutions tailored to constrained hardware, (2) inconsistent reporting of resource and privacy trade-offs, and (3) limited robustness against adaptive attackers and realistic deployment noise. We conclude with actionable recommendations and a prioritized research roadmap. Furthermore, the multi-dimensional taxonomy we introduce provides a structured framework to guide design choices and systematically improve the comparability, deployability, and overall trustworthiness of anomaly detection systems for constrained IoT.

Telecom doi: 10.3390/telecom7010009

Authors: Hussein Ssali Yoshiki Kamiura Tatsuro Maeda Kazutoshi Kato

This work presents the fabrication and characterization of a bow-tie antenna integrated uni-traveling carrier photodiode (UTC-PD) on a silicon carbide (SiC) substrate for efficient terahertz (THz) wave generation. The proposed device exploits the superior thermal conductivity and mechanical robustness of SiC to overcome the self-heating limitations associated with conventional indium phosphide (InP)-based photodiodes. An epitaxial layer transfer technique was utilized to bond InP/InGaAs UTC-PD structures onto SiC. The study systematically examines the influence of critical geometric parameters, specifically the mesa diameter and length between the antenna arms, on the emitted THz intensity in the 300 GHz frequency band. Experimental results show that the THz radiation efficiency is primarily governed by the mesa diameter, reflecting the trade-off between light absorption, device capacitance, and bandwidth, while the length between the antenna arms exhibits only a weak influence within the investigated parameter range. The fabricated device demonstrates strong linearity between photocurrent and THz output power up to 7.5 mA, after which saturation occurs due to space-charge effects. This work provides crucial insights for optimizing SiC-based bow-tie antenna integrated UTC-PD devices to realize robust, high-power THz sources vital for future high-data-rate wireless communication systems such as beyond 5G and 6G networks.

Telecom doi: 10.3390/telecom7010008

Authors: Yitao Li Conglu Huang

Existing vehicle-mounted satellite terminals primarily rely on mechanical or purely analog electronically steered antennas. They lack protocol-level relay capability and usually provide only short-range hotspot connectivity. These limitations make it difficult for such systems to deliver stable, high-throughput satellite access for personal mobile devices in dynamic vehicular environments, especially in remote regions without terrestrial networks. This paper proposes an intelligent vehicle repeater for satellite networks (IVRSN) that builds a dedicated satellite–vehicle–device relay architecture. It enables reliable broadband connectivity for conventional mobile terminals without requiring specialized satellite hardware. The IVRSN consists of three key technical components. Firstly, a dual-mode relay coverage mechanism is designed to support energy-efficient in-vehicle access and extended out-of-vehicle coverage. Secondly, a DoA-assisted, attitude-compensated hybrid beamforming scheme is developed. It combines subspace-based direction estimation with inertial sensor measurements to maintain high-precision satellite pointing under vehicle dynamics. Finally, a bidirectional protocol conversion module is introduced to ensure compatibility between ground wireless protocols and satellite link-layer formats with integrity-checked data forwarding. Compared to existing solutions, the proposed IVRSN provides higher stability and broader device compatibility, making it a feasible solution for high-speed, high-quality communications in remote or disaster regions.

Telecom doi: 10.3390/telecom7010007

Authors: Olabisi Falowo Samuel Falowo

Sub-Saharan Africa (SSA), with an estimated population of 1.243 billion people as of December 2024, had the lowest mobile Internet penetration in the world at 29%, significantly below the global average of 58%. Moreover, SSA also had the lowest mobile data traffic per active smartphone, averaging 5 GB per month—about a quarter of the global average of 19 GB per month in 2024. This paper analyses the factors responsible for the low Internet penetration in SSA, which include limited Internet service availability, Internet device and service affordability, digital ability, government regulation and policy, and deficit of network-supporting infrastructure. The paper then discusses the popular Internet access networks in SSA and their limitations. It presents low Earth orbit (LEO) satellites as a possible access network for enhancing Internet penetration in SSA, giving examples of LEO network service deployment in some SSA countries. The paper discusses the feasible business models for LEO satellite Internet services in SSA, the challenges to LEO satellite service penetration, and possible solutions.

Telecom doi: 10.3390/telecom7010006

Authors: Abdullahi Rashid Adem Oswaldo González-Gaxiola Ahmed H. Arnous Lina S. Calucag Anjan Biswas

The current paper retrieves implicit quiescent soliton solutions to optical metamaterials with nonlinear chromatic dispersion with generalized temporal evolution. Seven forms of self-phase modulation structures, as proposed by Kudryashov with time, are taken up. The implemented integration algorithm is Lie symmetry. A few of the solutions are in quadratures, while others are in terms of special functions. We also characterize the parameters that constrain the existence of such solutions.

Telecom doi: 10.3390/telecom7010005

Authors: Nguyen Van Toan Nguyen Thi Hau Pham Minh Nam Pham Ngoc Son Tran Trung Duy

In this paper, we propose a two-way hybrid satellite–terrestrial relay scheme employing Fountain codes (FCs). In the proposed model, a satellite and a ground user exchange data through a group of terrestrial relay stations, in the presence of an eavesdropper. In the first phase, the satellite and the ground user simultaneously transmit their encoded packets to the relay stations. The relay stations then apply a successive interference cancelation (SIC) technique to decode the received packets. To reduce the quality of the eavesdropping links, a cooperative jammer is employed to transmit jamming signals toward the eavesdropper during the first phase. Next, one of the relay stations which can successfully decode the encoded packets from both the satellite and the ground user is selected for data forwarding, by using a partial relay selection method. Then, this selected relay performs an XOR operation on the two encoded packets, and then broadcasts the XOR-ed packet to both the satellite and the user in the second phase. We derive exact closed-form expressions of outage probability (OP), system outage probability (SOP), intercept probability (IP), and system intercept probability (SIP), and realize simulations to validate these expressions. This paper also studies the trade-off between OP (SOP) and IP (SIP), as well as the impact of various system parameters on the performance of the proposed scheme.

Telecom doi: 10.3390/telecom7010004

Authors: Michail Alexandros Kourtis Andreas Oikonomakis Achileas Economopoulos Michael C. Batistatos Gion Kalemai Averkios Vasalos George Xilouris Panagiotis Trakadas

This work presents an energy-efficient implementation of Unmanned Aerial Vehicle (UAV)-based systems over 5G networks with on-board accelerated processing capabilities and provides a preliminary evaluation of the integrated solution. The study is a two-fold comparative analysis focused on connectivity and edge processing for UAVs. Two discrete deployment scenarios are implemented, where standard 5G configuration with artificial neural network (ANN) processing is evaluated against 5G Reduced Capability (RedCap) connectivity, paired with Spiking Neural Networks (SNNs). Both proposed energy-efficient alternative solutions are designed to offer significant energy savings; this paper examines whether they are suitable candidates for energy-constrained environments, i.e., UAVs, and quantifies their impact on the overall energy consumption of the system. The integrated solution, with 5G RedCap/SNNs, achieves energy-use reductions approaching 60%, which translates to an approximate 35% increase in flight time. The experimental evaluations were performed in a real-world deployment using a 5G-equipped UAV with edge-processing capabilities based on NVIDIA’s Jetson Orin.

Telecom doi: 10.3390/telecom7010003

Authors: Curtis Rookard

Space and satellite-based systems have had a monumental impact on providing greater interconnectivity across the world. The usage of space and satellite-based systems has increased the ability to access internet resources even in remote areas. Unfortunately, these systems are subject to malicious and multi-faceted cyberattacks. Therefore, proper threat detection systems must be implemented to safeguard these space systems. In our study, we present our novel intrusion detection framework, SpIDER, a space satellite intrusion detection system using explainable reinforcement learning. SpIDER leverages the benefits offered by reinforcement learning and Shapley additive global explanations to improve both the performance and explainability of space-based intrusion detection. We compare our SpIDER framework to several popular machine learning algorithms using the STIN and NSL-KDD datasets. We observe that our SpIDER framework achieves high performance, with accuracy and G-Mean above 99.98% on the STIN satellite dataset. SpIDER also outperforms other machine learning models on the NSL-KDD local area network dataset, achieving accuracy of 76.71% and a G-Mean of 80.49%. These results demonstrate that our SpIDER explainable deep reinforcement learning framework can perform as well or better than supervised machine learning models on both satellite-style and local area network data.

Telecom doi: 10.3390/telecom7010002

Authors: Christos Koukaras Stavros G. Stavrinides Euripides Hatzikraniotis Maria Mitsiaki Paraskevas Koukaras Christos Tjortjis

The increasing integration of Artificial Intelligence (AI) in education (AIEd) and its dependence on contemporary communication infrastructures (5G/6G, the Internet of Things (IoT), and Multi-Access Edge Computing (MEC)) has prompted a surge of research into applications, infrastructural dependencies, and deployment constraints. This is giving rise to a new paradigm termed AI-Enabled Telecommunication-Based Education (AITE). This review synthesises the recent literature (2022–2025) to examine how telecommunications and AI technologies converge to enhance educational ecosystems through adaptive learning systems, intelligent tutoring systems, AI-driven assessment, and administration. The findings reveal that low-latency, high-bandwidth connectivity, combined with edge-deployed analytics, enables real-time personalisation, continuous feedback, and scalable learning models that extend beyond traditional classrooms. In addition, persistent critical challenges are also reported, including issues with ethical governance, data privacy, algorithmic fairness, and uneven access to digital infrastructure, all affecting equitable adoption. By linking pedagogical transformation with telecom performance metrics—namely, latency, Quality of Service (QoS), and device interconnectivity—this work outlines a unified cross-layer framework for AITE. This review concludes by identifying future research avenues in ethical AI deployment, resilient architectures, and inclusive policy design to ensure transparent, secure, and human-centred educational transformation.

Telecom doi: 10.3390/telecom7010001

Authors: Nengjie Yu Dun Wang Xiaohui Ba Mingquan Lu Yantong Liu

The civil Global Navigation Satellite System (GNSS) signal is broadcast with an open structure, making it vulnerable to spoofing attacks. Incorporating authentication data into GNSS signals is a significant measure to enhance system security. Precise Point Positioning (PPP) technology has garnered extensive attention for its ability to provide real-time services with centimeter-level accuracy. The PPP service features a high data update rate, with the validity period of the data being approximately ten to twenty seconds. This imposes more stringent requirements on the authentication data rate and the authentication time. Code Shift Keying (CSK) technology has emerged as a key candidate for satellite-based PPP signal design, as it can increase the data rate without requiring additional spectrum resources. This paper investigates authentication methods for CSK-modulated satellite-based PPP signals. Two approaches are proposed: phase modulation authentication and polarity modulation authentication. Simulation and analysis results indicate that the PPP signal with phase modulation authentication experiences less carrier-to-noise ratio (C/N0) loss and has a higher detection probability. In contrast, the signal with polarity modulation authentication does not suffer from C/N0 loss and achieves a higher data rate and a shorter authentication time. These findings can serve as valuable references for future GNSS signal design.

Telecom doi: 10.3390/telecom6040099

Authors: Jong-Hoon Youn Woosik Lee Teuk-Seob Song

In a long-term monitoring wireless sensor network (WSN) application, sensors are frequently deployed in a wide and an unattended geographical area to gather useful information for a long period of time. Although energy efficiency is affected by various factors, the wireless communication unit is typically the most energy-intensive component of wireless sensors. To extend the life of wireless sensors, they alternate between sleep and active modes to conserve energy. Thus, to exchange a message with neighboring sensors, both sending and receiving sensors must discover each other and stay awake simultaneously. This paper proposes a new neighbor discovery protocol (NDP) by enhancing U-Connect, a well-known protocol that constructs neighbor discovery schedules using only a single prime number. Although the proposed method shares the same characteristics as U-Connect, it offers greater flexibility than U-Connect in terms of duty cycles and schedule lengths. Our numerical analysis based on a power-latency (PL) product shows that the proposed method is more efficient than other NDPs such as Quorum, U-Connect, Disco, and ECNDP.

Telecom doi: 10.3390/telecom6040100

Authors: Francis Kagai Philip Branch Jason But Rebecca Allen

Telecommunication infrastructures rely on cryptographic protocols designed for long-term confidentiality, yet data exchanged today faces future exposure when adversaries acquire quantum or large-scale computational capabilities. This harvest-now, decrypt-later (HNDL) threat transforms persistent communication records into time-dependent vulnerabilities. We model HNDL as a temporal cybersecurity risk, formalizing the adversarial process of deferred decryption and quantifying its impact across sectors with varying confidentiality requirements. Our framework evaluates how delayed post-quantum cryptography (PQC) migration amplifies exposure and how hybrid key exchange and forward-secure mechanisms mitigate it. Results show that high-retention sectors such as satellite and health networks face exposure windows extending decades under delayed PQC adoption, while hybrid and forward-secure approaches reduce this risk horizon by over two-thirds. We demonstrate that temporal exposure is a measurable function of data longevity and migration readiness, introducing a network-centric model linking quantum vulnerability to communication performance and governance. Our findings underscore the urgent need for crypto-agile infrastructures that maintain confidentiality as a continuous assurance process throughout the quantum transition.

Telecom doi: 10.3390/telecom6040098

Authors: Ivan Nedyalkov Georgi Georgiev

In this work, a hypothesis is studied as to whether different DoS attacks affect the parameters of voice and video streams, as well as performance, on three different VoIP platforms. This research is a continuation of a previous work, which studied the same hypothesis on the Asterisk FreePBX platform. The studied VoIP platforms are VitalPBX, Issabela, and CompletePBX 5, which are based on Asterisk Free PBX. For the purpose of this research, a simple model of an IP network was developed in the GNS3 IP network modeling platform. The experimental part of this work is conventionally divided into two parts. In the first part, only voice/video streams are exchanged in the network, and the studied VoIP server is not under DoS attacks. In the second part, the studied VoIP server is subjected to DoS attacks. The results obtained confirm the results of the previous research—the performance of the three studied platforms is not affected by DoS attacks. The attacks do not affect the parameters of the VoIP flows—the mean jitter value is below the permissible value of 30 ms; for the three servers, it is around 4 ms. The percentage of lost packets is again below the permissible value of 1%; for the three servers, it is around 0.5%. Despite sustained packet flooding, all three servers remained operational, and VoIP calls were maintained without significant degradation.

Telecom doi: 10.3390/telecom6040097

Authors: Abolaji Okikiade Ilori Kamoli Akinwale Amusa Tolulope Christiana Erinosho Agbotiname Lucky Imoize Olumayowa Ayodeji Idowu

The global shift to digital terrestrial television broadcasting (DTTB) from the conventional analogue has significantly transformed television culture, necessitating comprehensive technical and infrastructural evaluations. This study addresses the limitations of existing path-loss models for accurately predicting path loss in digital terrestrial television broadcasting in the UHF bands, motivated by the need for reliable, location-specific models that account for seasonal, meteorological, and topographical variations in Abeokuta, Nigeria. The study focuses on path-loss prediction in the UHF band using Ogun State Television (OGTV), Abeokuta, Nigeria, as the transmission source. Eight receiving sites, spaced 2 kilometers apart, were selected along a 16.7 km transmission contour. Daily measurements of received signal strength (RSS) and weather conditions were collected over one year. Seasonal path-loss models PLwet for the wet season and PLdry. For the dry season, models were developed using multiple regression analysis and further optimized using least squares (LS) and gradient descent (GD) techniques, resulting in six refined models: PLwet, PLdry, PLwet−LS, PLdry−LS, PLwet−GD, and PLdry−GD. Model performance was evaluated using Mean Absolute Error, Root Mean Square Error, Coefficient of Correlation, and Coefficient of Multiple Determination. Results indicate that the Okumura model provided the closest approximation to measured RSS for all the receiving sites, while the Hata and COST-231 models were unsuitable. Among the developed models, PLwet (RMSE− 1.2633, MAE − 0.9968, MSE − 1.5959, R − 0.9935, R2 − 0.9871) and PLdry−LS(RMSE− 1.1884, MAE − 0.7692, MSE − 1.4124, R − 0.9942, R2 − 0.9883) were found to be the most suitable models for the wet and dry seasons, respectively. The major influence of location-based elevation and meteorological data on path-loss prediction over digital terrestrial television broadcasting communication lines in Ultra-High-Frequency bands was evident.

Telecom doi: 10.3390/telecom6040096

Authors: Udara Jayasinghe Thanuj Fernando Anil Fernando

This paper proposes a quantum multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) framework for image transmission, which combines quantum multi-qubit encoding with spatial and frequency diversity to enhance noise resilience and image quality. The system utilizes joint photographic experts group (JPEG), high efficiency image file format (HEIF), and uncompressed images, which are first source-encoded (if applicable) and then processed using classical channel encoding. The channel-encoded bitstream is mapped into quantum states via multi-qubit encoding and transmitted through a 2 × 2 MIMO system with varied diversity schemes. The spatially mapped qubits undergo the quantum Fourier transform (QFT) to form quantum OFDM subcarriers, with a cyclic prefix added before transmission over fading quantum channels. At the receiver, the cyclic prefix is removed, the inverse QFT is applied, and the quantum MIMO decoder reconstructs spatially diverged quantum states. Then, quantum decoding reconstructs the bitstreams, followed by channel decoding and source decoding to recover the final image. Experimental results show that the proposed quantum MIMO-OFDM system outperforms its classical counterpart across all evaluated diversity configurations. It achieves peak signal-to-noise ratio (PSNR) values up to 58.48 dB, structural similarity index measure (SSIM) up to 0.9993, and universal quality index (UQI) up to 0.9999 for JPEG; PSNR up to 70.04 dB, SSIM up to 0.9998, and UQI up to 0.9999 for HEIF; and near-perfect reconstruction with infinite PSNR, SSIM of 1, and UQI of 1 for uncompressed images under high channel noise. These findings establish quantum MIMO-OFDM as a promising architecture for high-fidelity, bandwidth-efficient quantum multimedia communication.

Telecom doi: 10.3390/telecom6040095

Authors: Kidsanapong Puntsri Wannaree Wongtrairat

Underwater marine and freshwater environments are vast and mysterious, but our ability to explore them is limited by the inflexibility and inconvenience of monitoring systems. To overcome this problem, in this work, we present a proof-of-concept deployment of a real-time Internet of Underwater Things (IoUT) using blue light-emitting-diode-based visible light communication (VLC). Pulse-amplitude modulation with four levels is employed. To relax the focus point and increase the received power, four avalanche photodiodes (APDs) are adopted. Moreover, to reduce the error rate, the convolutional code with constraint-7 is used, which is the simplest to implement. Encoding and decoding are implemented by a field-programmable gate array. The results are verified by experimental demonstration. A baud rate of 9600 is used, but, unfortunately, we only have a 2 m long tank. System performance is improved when the number of APDs is increased; we investigated the effects of up to four APDs. Notably, bit error-free data transmission can be achieved. Additionally, this method would make underwater monitoring very conventional and dependable, and low-cost real-time monitoring would be possible, with data shown on the Grafana dashboard tool.

Telecom doi: 10.3390/telecom6040094

Authors: Jiawen Yi Tianhao Fang Li Chai Wenjin Wang Yi Zheng

Time division duplexing (TDD) technology holds great promise for future satellite communication systems. To address the interference and low resource utilization encountered in satellite TDD scenarios, this paper proposes a flexible and on-demand frame structure, where the interference can be mitigated by scheduling the UE transmissions instead of configuring a long guard period (GP). Based on the frame structure, the interference between downlink broadcasting signals and preambles is analyzed, followed by formulating a random access channel (RACH) occasion (RO) configuration optimization problem that aims to maximize the RO utilization, and a structured global candidate exploration algorithm (SGCEA) is proposed to solve it. Some simulation experiments are carried out based on the practical configurations from the third-generation partnership project (3GPP)standards. Simulation results show that the proposed algorithm consistently identifies the optimal RO configuration from the predefined configurations, and the utilization remains above 80% as the satellite coverage area increases, which demonstrates the superior performance of the proposed approach and highlights its potential for practical deployment in future TDD-based satellite communication systems.

Telecom doi: 10.3390/telecom6040093

Authors: Charalampos Papapavlou Konstantinos Paximadis Dan M. Marom Ioannis Tomkos

Emerging services such as artificial intelligence (AI), 5G, the Internet of Things (IoT), cloud data services and teleworking are growing exponentially, pushing bandwidth needs to the limit. Space Division Multiplexing (SDM) in the spatial domain, along with Ultra-Wide Band (UWB) transmission in the spectrum domain, represent two degrees of freedom that will play a crucial role in the evolution of backbone optical networks. SDM and UWB technologies necessitate the replacement of conventional Wavelength-Selective-Switch (WSS)-based architectures with innovative optical switching elements capable of handling both higher port counts and flexible switching across various granularities. In this work, we introduce a novel Photonic Integrated Circuit (PIC)-based switching element called flex-Waveband Selective Switch (WBSS), designed to provide flexible band switching across the UWB spectrum (~21 THz). The proposed flex-WBSS supports a hierarchical three-layered Multi-Granular Optical Node (MG-ON) architecture incorporating optical switching across various granularities ranging from entire fibers and flexibly defined bands down to individual wavelengths. To evaluate its performance, we develop a custom network simulator, enabling a thorough performance analysis on the critical performance metrics of the node. Simulations are conducted over an existing network topology evaluating three traffic-oriented switching policies: Full Fiber Switching (FFS), Waveband Switching (WBS) and Wavelength Switching (WS). Simulation results reveal high Optical-to-Signal Ratio (OSNR) and low Bit Error Rate (BER) values, particularly under the FFS policy. In contrast, the integration of the WBS policy bridges the gap between existing WSS- and future FFS-based architectures and manages to mitigate capacity bottlenecks, enabling rapid scalable network upgrades in existing infrastructures. Additionally, we propose a probabilistic framework to evaluate the node’s bandwidth utilization and scaling behavior, exploring trade-offs among scalability, component numbers and complexity. The proposed framework can be easily adapted for the design of future transport optical networks. Finally, we perform a SWaP-C (Size, Weight, Power and Cost) analysis. Results show that our novel MG-ON achieves strong performance, reaching a throughput exceeding 10 Pb/s with high OSNR values ≈14–20 dB and BER ≈10−9 especially under the FFS policy. Moreover, it delivers up to 7.5× cost reduction compared to alternative architectures, significantly reducing deployment/upgrade costs while maintaining low power consumption.

Telecom doi: 10.3390/telecom6040092

Authors: Maryam Jahanbakhshi Ivo Vertat

Phased antenna arrays have revolutionized modern wireless systems by enabling dynamic beamforming, multibeam synthesis, and user tracking to enhance data rates and reduce interferences, yet their reliance on expensive active components (e.g., phase shifters, amplifiers) embedded in antenna array elements limits adoption in cost-sensitive sub-GHz applications. Therefore, the active phased antenna arrays are still considered as high-end technology and primarily designed only for high-frequency bands and demanding applications such as radars and mobile base stations in microwave bands. In contrast, various important radio communication services still operate in sub-GHz bands with no adequate solution for modern antenna systems with beamforming capability. This paper introduces a 3D antenna array with switched-beam or multibeam capability, designed to eliminate mechanical rotators and active circuitry while maintaining all-sky coverage. By integrating collinear radiating elements with a Butler matrix feed network, the proposed 3D array achieves transmit/receive multibeam operation in the 435 MHz amateur satellite band and adjacent 433 MHz ISM band. Simulations demonstrate a design that provides selectable eight beams, enabling horizontal 360° coverage with only one radio connected to the Butler matrix. If eight noncoherent radios are used simultaneously, the proposed antenna array acts as a multibeam all-sky coverage antenna. Innovations in our design include a 3D circular collinear topology combining the broad and adjustable elevation coverage of collinear antennas with azimuthal beam steering, a passive Butler matrix enabling bidirectional transmit/receive multibeam operation, and scalability across sub-GHz bands where collinear antennas dominate (e.g., Lora WAN, trunked radio). Results show sufficient gain, confirming feasibility for low-earth-orbit satellite tracking or long-range IoT backhaul, and maintenance-free beamforming solutions in sub-GHz bands. Given the absence of practical beamforming or multibeam-capable solutions in this frequency band, our novel concept—featuring non-coherent cooperation across multiple ground stations and/or beams—has the potential to fundamentally transform how the growing number of CubeSats in low Earth orbit can be efficiently supported from the ground segment perspective.

Telecom doi: 10.3390/telecom6040091

Authors: Smitha Shivshankar Padmaja Kar Nirmal Acharya

As the global deployment of fifth-generation (5G) networks matures, the research community is conceptualising sixth-generation (6G) systems, projected for deployment around 2030. This article presents a comprehensive, evidence-based examination of the technological innovations and applications that characterise this transition, informed by a scoping review of 57 sources published between January 2020 and August 2025. The transition to 6G signifies a fundamental transformation from a mere communication utility to an intelligent, sensing, and globally integrated cyber-physical continuum, propelled by a strategic reassessment of the network’s societal function and the practical insights gained from the 5G era. We critically analyse the foundational physical layer technologies that facilitate this vision, including Reconfigurable Intelligent Surfaces (RIS), Terahertz (THz) communications, and the transition to Extremely Large-Scale MIMO (XL-MIMO), emphasising their interdependencies and the fundamental shift towards near-field physics. The analysis encompasses the architectural transformation necessary to address this new complexity, elucidating the principles of the AI-native network, the seamless integration of Non-Terrestrial Networks (NTN) into a cohesive three-dimensional framework, and the functional convergence of communication and sensing (ISAC). We also look at how these changes affect the real world by looking at data from trials and case studies in smart cities, intelligent transportation, and digital health. The article synthesises the overarching challenges in security, sustainability, and scalability, arguing that the path to 6G is defined by two intertwined grand challenges: building a trustworthy and sustainable network. By outlining the critical research imperatives that stem from these challenges, this work offers a holistic framework for understanding how these interconnected developments are evolving wireless networks into the intelligent fabric of a digitised and sustainable society.

Telecom doi: 10.3390/telecom6040090

Authors: Elsayed M. E. Zayed Mona El-Shater Ahmed H. Arnous Seithuti P. Moshokoa Anjan Biswas

This paper recovers dispersive gap solitons with the Kerr law of self-phase modulation and dispersive reflectivity. The enhanced direct algebraic method and the modified version of the sub-ODE approach have collectively made this retrieval possible. The intermediary solutions are the double-periodic functions that yielded the soliton solutions when the modulus of ellipticity approached unity. The Weierstrass elliptic function is the other form of intermediary function recovered from the model that also yielded soliton solutions as its special case.

Telecom doi: 10.3390/telecom6040089

Authors: Nthambeleni Reginald Netshikweta Mbuyu Sumbwanyambe Thanyani Pandelani

In densely populated areas, resource management is a challenge when mobile users in a session increase. The result of this is high inter-cell interference. Since interference is a function of power, we develop power control models aimed at addressing inter-cell interference among macrousers and femtousers in a 5G network. The models consider both cooperative and noncooperative game-theoretic theories. These are implemented within the framework of system dynamics. The models are developed using feedback loops and system dynamics approaches. The game-theoretic models are verified to establish a basis for developing mathematical models to implement power control in 5G networks. The comparative simulation demonstrates the superiority of cooperative game-theoretic power control in 5G NR in terms of signal-to-interference-plus-noise ratio (SINR), data rate, spectral efficiency (SE), and utility in interference-prone environments. While noncooperative strategies offer simplicity and lower signaling overhead, they result in poorer performance due to unmanaged interference and selfish resource utilization. The results demonstrate that the cooperative game-theoretic power control technique substantially enhanced network performance, achieving an average SINR improvement of 58.82% and an average SE improvement of 69.03%.

Telecom doi: 10.3390/telecom6040088

Authors: Muhammad Hassan

Wireless sensor networks (WSNs) have emerged as vital technologies for safety-critical applications due to their flexibility, scalability, and reliability. However, existing models such as LEACH, SEP, and TSEP exhibit limitations in energy efficiency, stability, and adaptability to heterogeneous node conditions. To address these gaps, this research proposes a multilevel heterogeneity-based WSN model that optimizes cluster-head (CH) selection and energy utilization for enhanced network performance. Simulations were conducted in MATLAB under unequal energy level variations and compared with established protocols. Results demonstrate that the proposed model consistently outperforms existing approaches in terms of network lifetime, throughput, and energy efficiency. Statistical analysis reveals a best-case improvement of approximately 9000 rounds and a worst-case gain of about 3000 rounds when four heterogeneity levels are employed, compared to three levels. These findings highlight that both the degree of energy diversity and the distribution of energy nodes across levels are crucial for achieving optimal performance. Overall, the proposed architecture significantly enhances reliability, stability, and energy efficiency, making it well-suited for disaster management and other safety-critical applications.

Telecom doi: 10.3390/telecom6040087

Authors: Suguna Gunasekaran Manikandan Chinnusami Rajesh Anbazhagan Kondreddy Dharani Surya Manasa Kakularam Sai Neha Reddy

A Global Navigation Satellite System (GNSS) and Long Range (LoRa) technology play a crucial role in connected vehicles. The demand for antennas that cover both LoRa and GNSS bands is increasing. This work has developed a novel dual-band coplanar waveguide (CPW)-fed interleaved meander line antenna, incorporating a radiating element, ground plane, and feed. The antenna dimension is 90 × 90 × 1.635 mm3. The design employs a planar meander line configuration to effectively cover the 868 MHz LoRa and 1248 MHz GNSS bands. The antenna was integrated with a Frequency Selective Structure (FSS) to improve the parameters. The designed antenna provides sufficient bandwidth of 40 and 110 MHz for the LoRa and GNSS frequency bands, respectively. The CPW-interleaved meander line antenna attains a gain of −0.12 dBi at LoRa and 3.5 dBi at GNSS frequency. It achieves a voltage standing wave ratio of <2 and impedance of 50 Ω. The novelty of the proposed work is integrating FSS with a CPW-interleaved meander line antenna, which achieves dual-band operation. This dual-band low-profile configuration is suitable for connected vehicle communication.

Telecom doi: 10.3390/telecom6040086

Authors: Mónica Zabala Haro Ángel Martín Furones María Jesús Jiménez-Martínez Ana Anquela Julián

A differential global navigation satellite system (DGNSS) improves the accuracy of conventional GNSS by utilizing reference stations to provide real-time correction data for positioning errors. In mobile networks, positioning methods based on signal parameters and location servers assist GNSS receivers by supplying correction information to mitigate errors from satellite clock inaccuracies, atmospheric disturbances, and orbital deviations. Depending on the configuration between the receiver and transmitter, base station and receiver clock errors are effectively eliminated. Proposed positioning algorithms leveraging mobile network observations in both coordinate and range domains demonstrate performance comparable to DGNSS solutions, offering a viable alternative for positioning in GNSS-denied environments. Experimental evaluations are conducted in outdoor scenarios under static conditions to validate the approach.

Telecom doi: 10.3390/telecom6040085

Authors: George Routis Ioanna Roussaki

Undoubtedly, the use of a keyboard is rather common when using a PC, laptop, terminal, or server. Unfortunately, when using wired or unencrypted wireless keyboards, all keystrokes can be eavesdropped using a simple RF scanner. The research presented in this paper aims to tackle this problem, or better security “gap”, in order to secure the respective keyboard communication. Five solutions are presented for securing the keystrokes when using a wired USB keyboard with encryption, a fiber optic cable, and a wireless connection (either microwave or light). The proposed solutions are novel, aiming at securing the communication between the USB keyboard and the end PC/laptop/cloud, since the commercial keyboards either wired or wireless are an easy target for an eavesdropper, as stated in the relevant literature section. There are detailed diagrams illustrating the circuits and modules used, while the respective block diagrams and message details are also provided. In conclusion, challenges are studied and addressed, experiments are carried out, and suitable solutions are presented.

Telecom doi: 10.3390/telecom6040084

Authors: Roland N. Mfondoum Nikol Gotseva Atanas Vlahov Antoni Ivanov Pavlina Koleva Vladimir Poulkov Agata Manolova

Mobile networks have advanced significantly, providing high-throughput voice, video, and integrated data access to support connectivity through various services to facilitate high user density. This traffic growth has also increased the complexity of outlier detection (OD) for fraudster identification, fault detection, and protecting network infrastructure and its users against cybersecurity threats. Autoencoder (AE) models are widely used for outlier detection (OD) on unlabeled and temporal data; however, they rely on fixed anomaly thresholds and anomaly-free training data, which are both difficult to obtain in practice. This paper introduces statistical masking in the encoder to enhance learning from nearly normal data by flagging potential outliers. It also proposes a quasidynamic threshold mechanism that adapts to reconstruction errors, improving detection by up to 3% median area under the receiver operating characteristic (AUROC) compared to the standard 95% threshold used in base AE models. Extensive experiments on the Milan Human Telecommunications Interaction (HTA) dataset validate the performance of the proposed methods. Combined, these two techniques yield a 31% improvement in AUROC and a 34% lower computational complexity when compared to baseline AE, long short-term memory AE (LSTM-AE), and seasonal auto-regressive integrated moving average (SARIMA), enabling efficient OD in modern cellular networks.

Telecom doi: 10.3390/telecom6040083

Authors: Marcelo B. Perotoni Julien Huillery

Time reversal techniques have been investigated for ultrasound and electromagnetic waves. They offer some advantages, particularly in cluttered and inhomogeneous environments, for point-to-point applications. The instrumentation usually employed for electromagnetic time reversal involves costly vector network analyzers, different interconnected generators and receivers, or a base station for mobile phones. This article explores the use of a low-cost commercial software-defined radio, in frequencies between 700 MHz and 2100 MHz, with indoor tests showing its performance and observed voltage gains for the received pulse.

Telecom doi: 10.3390/telecom6040082

Authors: Niken Prasasti Martono Ryota Tsukamoto Hayato Ohwada

The Internet of Things (IoT) offers promising solutions for smart agriculture, particularly in the monitoring of livestock. This paper proposes a contactless, low-cost system for individual cow identification and monitoring in a dairy barn using a single Pan–Tilt–Zoom (PTZ) camera and a YOLOv8 deep learning model. The PTZ camera periodically scans the barn, capturing images that are processed to detect and recognize a specific target cow among the herd without any wearable sensors. The system embeds barn area metadata in each image, allowing it to estimate the cow’s location and compute the frequency of its presence in predefined zones. We fine-tuned a YOLOv8 object detection model to distinguish the target cow, achieving high precision in identification. Experimental results in a real barn environment demonstrate that the system can identify an individual cow with 85.96% Precision and 68.06% Recall, and the derived spatial occupancy patterns closely match ground truth observations. Compared to conventional methods requiring multiple fixed cameras or RFID-based wearables, the proposed approach significantly reduces equipment costs and animal handling stress. It should be noted that the present work serves as a proof-of-concept for targeted cow tracking that identifies and follows a specific individual within a herd rather than a fully generalized multi-cow identification system.

Telecom doi: 10.3390/telecom6040081

Authors: Marco Krondorf

Wireless low earth orbit (LEO) satellite communication ground terminals need to perform an initial time and frequency synchronization to access to the LEO system. Initial synchronization consists of three steps: detecting the presence of the LEO satellite downlink signal, synchronizing the terminal receiver to the current Doppler frequency shift and performing Doppler pre-compensation before uplink signal transmission, and ensuring low probability of false alarm at low SNR in the LEO uplink receiver. This article explains this three step synchronization procedure in detail. The major advantage is that the synchronization procedure can be carried out even without a priori knowledge of the satellite orbit ephemeris or any sort of GNSS navigation data. Initial synchronization is of particular importance for typical LEO uplink signals which are formed of short radio bursts. The packet detection in burst traffic radio systems is a crucial task to accomplish start of frame detection. It triggers the start of the digital receiver algorithms to demodulate the incoming uplink burst. The packet detection is accomplished by cross-correlation and threshold detection which show significant probability of false alarm in low signal to noise (SNR) regions. Hence, before running a stable uplink connection, the terminal must accomplish the proposed initial synchronization procedure, as outlined in this article.

Telecom doi: 10.3390/telecom6040080

Authors: Fayez Gebali Alshimaa Magdy

RSA’s modular exponentiation is the basic operation in public key infrastructure and is naturally the target of side-channel attacks. In this work we propose two algorithms that defeat side-channel attacks: Paired Permutation Exponentiation (PPE) and Permute, Split, and Accumulate (PSA). We compare these two algorithms with the classic right-to-left technique. All three implementations are evaluated using Intel® Performance Counter Monitor (PCM) at an effective 0.25 ms sampling interval. We use fixed 2048-bit inputs, pin the Python 3.9.13 process to a single core Intel® Core™ i5-10210U, and repeat each experiment 100 and 1000 times to characterize behavior and ensemble statistics. Our proposed technique PSA shows the lowest runtime and the strongest hardening against per-bit correlation relative to the standard RtL. Residual leakage related to the Hamming weight of the exponent may remain observable but the only information gathered is the the Hamming weight of the secret key. The exact location of the secret key bits is completely obscured.

Telecom doi: 10.3390/telecom6040079

Authors: Mohammed Rashed Yasser Essa

The rapid and ongoing adoption of smart home products, coupled with the increasing integration of artificial intelligence (AI), particularly in these products, is an undeniable reality. However, as both technologies converge, they also give rise to a range of significant concerns. The EU’s recent AI Act specifically addresses the challenges associated with the use of AI technology. In this study, we examine three AI-integrated products with toy capabilities that are sold in Spain, serving as a case study for the EU market of smart home devices that incorporate AI. Our research aims to identify potential compliance issues with both the AI Act and the General Data Protection Regulation (GDPR). Our results reveal a clear and worrying gap between the existing legislation and the functionalities of these devices. Using a normal user’s approach, we find that the privacy policies for these products, whose features make them high-risk AI systems, AI systems with systemic risk, or both as per the AI Act, fail to provide any information about AI usage, particularly of ChatGPT, which they all integrate. This raises significant concerns, especially as the market for such products will continue to grow. Without rigorous enforcement of existing legislation, the risk of misuse of sensitive personal information becomes even greater, making strict regulatory oversight essential to ensure user protection.

Telecom doi: 10.3390/telecom6040078

Authors: Stella N. Arinze Augustine O. Nwajana

The growing global demand for secure, transparent, and efficient electoral systems has highlighted the limitations of traditional voting methods, which remain susceptible to voter impersonation, ballot tampering, long queues, logistical challenges, and delayed result processing. To address these issues, this study presents the design and implementation of a Radio Frequency Identification (RFID)-based electronic voting framework that integrates robust voter authentication, encrypted vote processing, and decentralized real-time monitoring. The system is developed as a scalable, cost-effective solution suitable for both urban and resource-constrained environments, especially those with limited infrastructure or inconsistent internet connectivity. It employs RFID-enabled smart voter cards containing encrypted unique identifiers, with each voter authenticated via an RC522 reader that validates their UID against an encrypted whitelist stored locally. Upon successful verification, the voter selects a candidate via a digital interface, and the vote is encrypted using AES-128 before being stored either locally on an SD card or transmitted through GSM to a secure backend. To ensure operability in offline settings, the system supports batch synchronization, where encrypted votes and metadata are uploaded once connectivity is restored. A tamper-proof monitoring mechanism logs each session with device ID, timestamps, and cryptographic checksums to maintain integrity and prevent duplication or external manipulation. Simulated deployments under real-world constraints tested the system’s performance against common threats such as duplicate voting, tag cloning, and data interception. Results demonstrated reduced authentication time, improved voter throughput, and strong resistance to security breaches—validating the system’s resilience and practicality. This work offers a hybrid RFID-based voting framework that bridges the gap between technical feasibility and real-world deployment, contributing a secure, transparent, and credible model for modernizing democratic processes in diverse political and technological landscapes.