In resource-constrained unmanned aerial vehicle (UAV) infrared image transmission, infrared small targets usually occupy only a small number of pixels, while conventional uniform resource allocation strategies fail to consider the semantic differences among image regions, resulting in inefficient resource utilization and loss of target-related information. To address this issue, this paper proposes a semantic-aware resource allocation method for infrared small-target detection. First, infrared images are divided into multiple grid cells, and the semantic importance of each cell is estimated based on preliminary detection results. Then, the cells are classified into different semantic levels, followed by differentiated bit allocation and region-wise reconstruction. Finally, a joint optimization problem of grid partitioning and resource allocation parameters is formulated and solved by the covariance matrix adaptation evolution strategy (CMA-ES) to optimize both detection performance and image reconstruction quality. Experimental results demonstrate that the proposed method achieves mIoU values of 0.6106 and 0.7445 at available bit rates of 12.5% and 50%, respectively, showing significant improvements over comparison methods. Moreover, CMA-ES obtains stable optimization results with fewer evaluations and provides a favorable balance between detection accuracy and reconstruction quality. These results indicate that the proposed method can effectively preserve target-related semantic information under limited communication resources, providing an effective solution for efficient UAV infrared image transmission and reliable small-target detection.
This paper presents a multi-dimensional robustness evaluation framework for ultra-dense Internet of Things (UD-IoT) networks in smart city environments. The framework addresses the need to assess robustness beyond isolated indicators such as latency, throughput, packet loss, or availability by integrating operational continuity (OC), scalable resource efficiency (SRE), network flexibility (NF), and security (SEC) into a unified robustness metric (R). Building on an earlier formulation of the metric, the present study extends its interpretation toward smart city IoT deployments, strengthens the perception layer perspective, and introduces a controlled fifth-generation (5G) connectivity validation procedure. The evaluation is performed in two complementary stages. First, simulation-based analysis is conducted across critical, essential, and basic deployment scenarios to examine the mathematical behavior of the metric under different operational priorities and parameter ranges. Second, a validation setup based on free5GC and UERANSIM is used to transform observable control-plane, user-plane, service-connectivity, and security indicators into comparable robustness components through an explicit normalization and mapping procedure. The results show scenario-dependent robustness degradation across the evaluated smart city IoT conditions, with the strongest degradation in the combined stress scenario where endpoint density, traffic intensity, service recovery stress, and abnormal access conditions occur simultaneously.
This paper proposes a novel framework integrating Holographic Intelligent Surfaces (HISs) with magnetic energy harvesting at the source node to enable energy-efficient wireless communication. In the proposed system, energy harvesting is performed at the transmitter using magnetic field coupling, eliminating reliance on conventional power supplies. The harvested energy is then used to generate and transmit signals, which are intelligently manipulated by the HIS to enhance propagation conditions between the source and the destination. By leveraging the continuous electromagnetic control capability of HIS, the system improves signal strength, coverage, and reliability. Analytical insights highlight the potential of combining magnetic energy harvesting with HIS to support sustainable and self-powered wireless networks, particularly for next-generation communication systems.
Reliable dynamic spectrum access in wide-area cognitive radio networks (CRNs) is challenged by sparse and erroneous spectrum-sensing measurements. This work formulates spatial-spectrum occupancy reconstruction as an image inpainting problem and proposes two cascaded deep learning models: a bidirectional long short-term memory-based image inpainting model (BiLSTM-IIM) and a binary diffusion-based image inpainting model (Diff-IIM). In both models, Stage 1 corrects sensing errors at observed locations, while Stage 2 reconstructs missing entries. The models are evaluated using simulations of a 2500m×2500m cognitive radio network with 50–200 secondary users, five primary users, and three spatial resolutions under representative wireless conditions. Both models generally outperform total variation and matrix completion baselines under sparse and noisy observations. Under moderate sensing errors, BiLSTM-IIM achieves accuracies of 92.91%, 93.66%, and 91.17% at the 10×10, 20×20, and 30×30 resolutions, respectively, while Diff-IIM achieves lower false-alarm rates with fewer parameters. Stage 1 reduces the sensing-error rate by approximately 58% for BiLSTM-IIM and 61% for Diff-IIM. These results support deep learning-based inpainting for wide-area spectrum occupancy reconstruction under the evaluated conditions.
In most railway divisions the results of scheduled inspections of automation and telemechanics field devices are still recorded on paper. Such records reach engineering management with a delay, are easy to lose, and are difficult to verify. This paper examines the data transmission core of a digital inspection complex in which passive RFID tags identify both the equipment and the personnel, and the inspection record is delivered to a cloud server over a hybrid cellular architecture combining a failure-prone GSM channel with a reliable CDMA channel. To quantify the stability of such a system, a continuous-time Markov chain model is constructed in which the link is represented as an M/M/2/K queue with one unreliable server: both channels carry traffic in parallel, and during a GSM outage the CDMA channel alone sustains service. Records already admitted are preserved across a channel switch; only records arriving at a full shared buffer are rejected. This residual overflow loss stays below 0.1% at routine load with a buffer of m ≥ 5 and reaches about 2.8% only under post-incident overload. The model parameters were measured on an operating ESP32-based scanner complex piloted at Hamza station on 46 point machines. Calculations for three load scenarios show that increasing the local buffer beyond m = 5 yields diminishing returns while the delay grows, and that resilience is governed primarily by the presence of the redundant channel and adequate buffering, with the primary-channel recovery rate a secondary factor.
Telecommunications infrastructures are increasingly cloud-native, multi-vendor, and mission-critical environments, integrating 5G core networks, virtualized network functions, and software-defined infrastructure that expand the operational attack surface and make robust security architecture a core engineering requirement. Zero-Trust Architecture (ZTA) has emerged as the leading technical paradigm for securing these environments through continuous authentication and policy-based access control; however, technical Zero-Trust controls alone do not guarantee successful deployment, and large-scale deployment introduces significant socio-technical and governance challenges that existing engineering-focused frameworks only partially address. This study makes two contributions: it provides longitudinal evidence on the impact of ZTA on organizational trust using an extended Technology Acceptance Model (TAM) that incorporates Perceived Trust, and it proposes a Proactive Trust Management Playbook (PTMP) for telecommunications infrastructure organizations that complements technical Zero-Trust deployments through organizational governance. The study draws on a five-wave repeated cross-sectional longitudinal case study conducted between 2020 and 2023 in a multinational telecommunications infrastructure organization. The five waves span three organizational phases, enabling an assessment of employee perceptions of usefulness, ease of use, and trust before and after ZTA deployment and following a structured governance intervention. The findings reveal a substantial decline in the composite TAM index following ZTA implementation (−24%, Cohen’s d = 1.12), with no meaningful spontaneous recovery over time (d = 0.08). A structured Communication Campaign was associated with a partial but incomplete recovery (d approximately 0.47), indicating that trust erosion under ZTA is measurable and suggesting that trust recovery is shaped more by governance interventions than by technological adaptation alone. The proposed PTMP complements technical Zero-Trust architectures by strengthening organizational trust and governance in telecommunications infrastructure environments.
Federated learning (FL) enables cellular traffic prediction without centralizing raw base-station data, but statistical heterogeneity makes a single global model unsuitable for many clients. This paper proposes federated clustering with adaptive personalization (FedCAP), a parameter-efficient personalized FL framework that separates cluster-level representation learning from client-level adaptation. Clients are grouped using training-only daily traffic profiles, after which an LSTM backbone is trained by FedAvg within each cluster. Each client then freezes the cluster backbone and optimizes a residual bottleneck adapter locally. The adapter contains 4241 trainable parameters, 6.19% of the 68,483-parameter three-feature backbone and prediction head, and personalization transmits no model updates. In a shared-seed-42 comparison across 11 methods and four public datasets, FedCAP ranks first or second in 12 of 16 dataset–metric combinations. Across five shared seeds, its mean MAE is 6.66%, 7.47%, 2.64%, and 3.86% below FedAvg on the Milan, Trentino, Bihar, and Taiwan datasets, respectively. Holm-adjusted paired t-tests identify 6 significant dataset–metric differences, whereas exact Wilcoxon tests are not significant because each comparison contains only five nonzero seed-matched pairs; the statistical evidence is therefore interpreted conservatively.
Low Earth orbit mobile satellite system (LEO-MSS) is a major system that provides communication support for mobile terminals beyond the coverage of terrestrial communication systems. However, passive handover happens frequently, caused by the quick movement of LEO satellites, making it hard to guarantee quality of service (QoS) for handover users while maintaining a large number of users. To tackle this problem, we propose a novel soft handover scheme and combine it with uplink power-domain non-orthogonal multiple access (PD-NOMA) for the first time to guarantee QoS for handover users and improve uplink throughput. We analyze the uplink PD-NOMA-based soft handover scheme with three users in two beams and give the closed-form expression of the optimal uplink transmission power allocation. Afterward, we introduce this method into a practical multi-beam LEO-MSS system with multiple users and sub-channels and formulate the optimization problems to maximize system throughput. Numerical results show that the proposed uplink PD-NOMA-based soft handover scheme provides much better performance on throughput and fairness for heavy loads.
5G New Radio Vehicle-to-Everything (NR V2X) networks must support heterogeneous traffic with strict and diverse latency requirements. Conventional proportional-fair (PF) scheduling does not explicitly account for packet deadlines, which can lead to deadline violations for critical vehicular services under congestion. This paper studies deadline-aware MAC scheduler-weight adaptation for 5G NR V2X using probabilistic prediction and reinforcement learning. We implement a closed-loop ns-3/5G-LENA framework in which network telemetry is exchanged with a Python control agent through ns3-ai shared memory. Gaussian Mixture Model (GMM), Hidden Markov Model (HMM), and Bayesian Logistic Regression (BLR) classifiers are used to predict imminent deadline violations. Their outputs are either mapped directly to scheduler weights or provided as additional state information to a Proximal Policy Optimization (PPO) agent. We evaluate ten scheduling strategies: PF, a non-learning Slack-Based Deadline-Aware Scheduler (SB-DAS), three classifier-only controllers, three classifier-assisted PPO variants, PPO-only, and PPO-only with safety shielding. Experiments are conducted across three vehicle densities and three random seeds per density, using the Deadline-Constrained Packet Reception Ratio (DC-PRR) as the main metric. The PF baseline achieves 61.55% mean DC-PRR and degrades from 75.2% at 30 vehicles to 44.1% at 60 vehicles. In contrast, all adaptive strategies exceed 95% mean DC-PRR and recover 34–38 percentage points over PF in every paired density/seed comparison. The main result is therefore the robust gap between PF and deadline-aware adaptation. Differences among the adaptive controllers are much smaller and fall within the observed seed-to-seed variability. In particular, SB-DAS, which uses no classifier, neural network, or training, achieves DC-PRR statistically indistinguishable from the learned and probabilistic controllers. This indicates that, in the evaluated scenarios, most of the gain comes from deadline awareness itself rather than from learning. We also find that adding classifier-derived violation probabilities to PPO does not consistently improve performance over PPO using raw telemetry alone. To support reproducibility and deployment assessment, the paper includes detailed parameter tables, reward-coefficient and sensitivity analysis, scheduler-weight sensitivity, and per-controller inference-latency and complexity measurements.
Conventional multiple-input multiple-output (MIMO) systems rely on static antenna placement. To exploit additional spatial degrees of freedom, the fluid antenna (FA) concept has emerged as a promising solution for improving data rates and diversity performance. Most existing FA studies assume Rayleigh fading, whereas analytical characterization under Nakagami-m fading is more challenging. This article investigates the ergodic capacity of FA-assisted MIMO systems over Nakagami-m fading channels. By applying majorization theory, upper and lower bounds on the ergodic capacity are derived. High signal-to-noise ratio (SNR) approximations are then obtained to clarify the role of the fading parameter and the number of propagation paths. The large-system behavior is also studied, and Monte Carlo simulations are used to assess the tightness of the proposed bounds. The results show that the upper bound closely tracks the simulated capacity, while the lower bound remains useful mainly in the low-SNR regime.
Air-core (donut-shaped) microwave beams are attractive for wireless power transfer (WPT) for drones because their central intensity null can reduce field concentration near mission equipment mounted near the drone center. This paper proposes a fixed air-core beam WPT architecture in which the transmitting beam is not electronically steered; instead, the drone maintains its position near an efficient receiving region using onboard control based on relative beam-position information inferred from received signals. To support this architecture, we present a theoretical analysis of captured power and spillover for a circular receiving aperture illuminated by a Laguerre–Gaussian (LG) beam. Rather than claiming a direct extension of the modified Friis formula to LG beams, we derive a closed-form expression corresponding to the edge-based efficiency/spillover interpretation used in Gaussian-beam WPT discussions. We then report staged experimental validation using a 24 GHz radial line slot antenna (RLSA)-based air-core beam transmitter with a 25 W class feed circuit, a horn-antenna-based reference receiver for principal validation, and a panel rectenna prototype for implementation-oriented evaluation. The results clarify practical operating conditions and implementation limitations, including distance-dependent position-detection behavior and compact-receiver sensitivity degradation under air-core beam illumination.
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.
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.
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.
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.
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.
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.
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.
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.
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.
