Telecom, Volume 7, Issue 4 (August 2026) – 28 articles

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

This paper presents a corrugated Vivaldi antenna architecture targeting sub-6 GHz and millimeter-wave frequency bands for 5G CubeSat applications, combining experimental validation at sub-6 GHz with a simulation-based scaling study at 26.5 GHz. Existing CubeSat antenna designs either target a single frequency band or rely on complex metamaterial structures incompatible with nanosatellite fabrication constraints. To address this gap, a single-element corrugated Vivaldi antenna measuring 90 mm × 80 mm is designed, fabricated on FR-4 substrate, and experimentally validated at 3.5 GHz, confirming a wide impedance bandwidth of 2.75 GHz and a peak gain of 9.6 dBi. The strong agreement between CST Studio Suite simulations and measurements validates the electromagnetic solver configuration, which is subsequently applied, as a simulation-based design study, to a geometrically scaled version on Taconic RF-60A substrate operating at 26.5 GHz. The miniaturized single-element version achieves a simulated 17 GHz ultra-wideband response and 6 dBi gain in a 7.32 mm × 6.32 mm footprint. Two- and four-element array configurations at 26.5 GHz demonstrate systematic simulated gain progression to 9 dBi and 13 dBi, respectively, with beamwidth narrowing from 49° to 30°. All 26.5 GHz designs are simulated with lossy copper metallization ($\sigma =5.8\times {10}^7$ S/m) and are entirely simulation-based; experimental mmWave validation is a designated target for future work. These results establish a validated design and scaling roadmap for corrugated Vivaldi antennas spanning sub-6 GHz and millimeter-wave bands, offering a cost-effective and CubeSat-compatible solution for high-data-rate inter-satellite communication links. Full article

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

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

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. Full article

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. Full article

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. Full article