Post-earthquake functionality assessment and emergency base station deployment optimization for communication systems

Efficient cell association remains a fundamental challenge in fifth-generation (5G) vehicle-to-everything (V2X) systems due to rapid topology changes, heterogeneous deployments, and stringent latency requirements. Conventional learning-based approaches often rely on shallow representations or independent optimization strategies, limiting their adaptability in dense and highly dynamic environments. To address these issues, this study introduces a multi-level graph representation framework that models interactions between vehicles and base stations across hierarchical spatial structures. The proposed approach integrates contextual node embedding with attention-driven graph learning to capture mobility patterns, signal characteristics, and network load dependencies. Additionally, a training-stage optimization mechanism is incorporated to refine attention parameters, improving convergence behavior without increasing inference complexity. The framework is evaluated using a real-world vehicular mobility dataset, demonstrating consistent improvements in association stability, handover reliability, and overall network performance compared with existing deep learning and graph-based methods. Experimental results show gains in accuracy (94.17%) and F1-score (93.93%), indicating enhanced decision robustness under dynamic conditions. Although validation is conducted on an urban dataset, the proposed architecture provides a scalable foundation for adaptive cell selection in next-generation intelligent transportation systems.

In wireless sensor networks (WSNs), transmission power adjustment has a direct impact on both node-level energy expenditure and overall network lifetime, while simultaneously shaping the communication conditions under which distributed data aggregation must operate. This coupling is especially important in energy-constrained settings with progressive node failures, where topology control affects not only connectivity but also the reliability of local information exchange. In this paper, we formulate the problem of distributed data aggregation in a WSN with strict local-information constraints, where nodes communicate only through one-hop broadcasts and adapt their transmission power according to the LINT protocol. The resulting communication graph is degree-regularized but not necessarily bidirectional, which makes the design and evaluation of aggregation protocols substantially different from the classical fixed-topology consensus setting. Within this formulation, we investigate the interplay between adaptive transmission power control and decentralized aggregation by comparing two local aggregation protocols, Metropolis and the Local Voting Protocol (LVP), under both adaptive-range and fixed-range communication regimes. Our contribution is twofold. First, we provide a problem formulation for joint adaptive communication and distributed aggregation in locally informed, energy-constrained WSNs. Second, using a simulation framework with a standard radio energy model, probabilistic reporting to a base station, and multiple node deployment scenarios, we show that the relative performance of aggregation protocols depends on the communication regime induced by power adjustment. In particular, Metropolis is competitive, and in some cases preferable, under fixed-range symmetric communication, whereas LVP yields more stable aggregation quality under LINT-based adaptive transmission power control while maintaining comparable network lifetime and energy efficiency.

Data routing protocols play a vital role in Wireless Sensor Networks (WSNs). However, large network sizes and constrained resources demand more energy-efficient routing strategies. In this context, conventional routing protocols often show weak load balancing and inefficient energy use. Low-Energy Adaptive Clustering Hierarchy (LEACH) and Low-Energy Adaptive Clustering Hierarchy Centralized (LEACH-C) remain the two most widely adopted hierarchical routing protocols in WSNs. LEACH operates as a non-geographic distributed routing protocol, whereas LEACH-C is a geographic-based centralized routing protocol. Compared with flat routing protocols, both can prolong network lifetime, but they still suffer from limited energy efficiency. To address this limitation, we in this research proposed an enhanced LEACH protocol based on cluster configuration and Quantum Beluga Whale Optimization (QBWO-LEACH). During the setup phase, the central base station (BS) employs the proposed QBWO approach, which integrates Beluga Whale Optimization (BWO) with the strengths of quantum computing, to centrally organize the clusters. This process includes determining the cluster centroids, assigning cluster members, and evaluating cluster energy, cluster priority, and cluster lifetime. In the cluster heads (CHs) rotation phase, local clusters use the position and energy information of all cluster members to perform distributed CHs switching, distributing cluster energy approximately evenly among all members. In the steady-state phase, the relay forwarding of monitored data flows is implemented. Compared with traditional LEACH and other improved variants of the LEACH protocols, the comprehensive performance of the protocol proposed in the present research is found to be superior. We compare our proposed QBWO-LEACH with the existing LEACH protocols in terms of node survival, network residual energy, half node dies (HND), last node dies (LND), and first node dies (FND), in all four cases using both simulation and statistical analysis. QBWO-LEACH demonstrates an average improvement of 51.87% over LEACH, 17.69% over Particle Filter LEACH (PF-LEACH) and 4.31% over a 2-stage Genetic Algorithm-based LEACH (GA2-LEACH) in node survival and network residual energy in all four cases.

This paper proposes a dynamic gain-adaptive scheme for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) based non-orthogonal multiple access (NOMA) networks (termed as ASRN) with a transmit antenna selection technique at the base station to serve two vehicular users. Unlike conventional fixed-ranking NOMA, ASRN dynamically allocates power and assigns successive interference cancellation order to users based on their channel conditions. Firstly, we derive closed-form expressions for the system outage probability, asymptotic outage behavior, and diversity order, revealing that the proposed scheme achieves a diversity gain scaling with the number of STAR-RIS elements. Then, a greedy algorithm is further proposed to jointly optimize antenna selection and NOMA users' pairing. Secondly, we introduce a key performance metric (termed as the ergodic sum rate (ESR)) and define the multiple averaging ergodic sum rate (MA-ESR) to evaluate the effectiveness of ASRN scheme in delay-tolerant networking approach, when compared to other schemes. Monte Carlo simulations demonstrate that ASRN significantly outperforms dynamic gain-adaptive STAR-RIS OMA conventional STAR-RIS NOMA/OMA, and decode-and-forward relaying schemes, offering superior outage performance, coding gains, ESR performance and MA-ESR performance, particularly in vehicular environments.

ABSTRACT User selection significantly influences the efficiency and reliability of cooperative non-orthogonal multiple access (CNOMA) systems, particularly when nodes operate under radio frequency energy harvesting (RFEH). This study investigates RFEH-enabled CNOMA networks, with and without simultaneous wireless information and power transfer (SWIPT), to assess the impact of various user selection strategies under energy constraints. The system comprises a base station (BS), multiple near users, and one far user, where near users harvest energy via SWIPT and assist the far user using amplify-and-forward (AF), decode-and-forward (DF), or hybrid AF/DF relaying. The BS transmits a superimposed signal, and the selected near user performs successive interference cancellation to decode its data and forward the far user ‘s information using harvested energy. System performance over Nakagami- m fading channels is analysed in terms of outage probability, throughput, and energy efficiency, with MATLAB simulations identifying the optimal relaying and user selection combination for energy-constrained SWIPT-assisted CNOMA systems.

Wireless sensor networks have the major challenge of ensuring energy efficiency, scalability, and data delivery. The achievement of these goals becomes challenging when the nodes have limited battery energy, the network topology changes frequently, and the data transfer is not distributed among the nodes. The routing protocols have been found wanting in various ways, as they consume more energy than required, have difficulties selecting the appropriate nodes as cluster heads, and cannot be adapted when the network topology changes frequently. This eventually reduces the lifespan of the network and affects its performance as a whole. To mitigate these problems, this paper presents a Reinforcement Learning-based Multipath Hybrid Whale-Grey Wolf Optimization framework. Deep Reinforcement Learning is incorporated throughout this framework for adaptive sleep scheduling and node activation. The DRL agent learn and schedules tasks based on residual energy, node centrality, and node proximity to the cluster head as primary factors. This research also suggests a new hybrid optimization algorithm, where the global search capability of the Whale Optimization Algorithm (WOA) and the strong convergence and exploitation capabilities of the Grey Wolf Optimizer (GWO) are hybridized for more effective cluster head selection. This hybridization process is done for a more effective and energy-efficient selection of cluster heads. It has also been proposed that a multipath routing technique will be used to develop multiple stable and energy-efficient paths between the cluster heads and the base station. The stability of the paths, the energy left, and the quality of the paths will be considered while establishing the paths using the hybrid WOA-GWO algorithm. The effectiveness of the proposed framework will be validated through extensive simulations, which prove that the proposed model performs well with a packet delivery ratio of 97.8%, total energy consumption of 320J, and a total throughput at the base station of 72 kbps.

The energy hole problem is a significant challenge in wireless sensor networks (WSN) that use multi-hop routing protocols. Nodes near the base station (BS) typically experience higher energy consumption due to higher data traffic, resulting in faster network energy depletion and creating an energy hole near the BS. To address this issue, the paper proposes a solution involving a mobile data collector (MDC) in an unequal grid cluster. The number and size of the clusters are determined based on the radio energy model's threshold transmission value, which provides balanced data traffic distribution in the network. The cluster head (CH) is elected based on the node's distance from the cluster centroid and its residual energy. Additionally, the frequency of CH rotation is optimized through an energy-efficient CH change mechanism. The inclusion of an MDC enables data collection from the CHs along the vertical boundaries, effectively reducing the occurrence of energy holes and extending the network's overall lifespan. Simulation results demonstrate the superior performance of our protocol compared to similar existing schemes. Our proposed work was simulated using OMNeT++, and the results indicate that it achieves approximately 21% less energy consumption than similar existing works.

The rapid growth of devices in the Internet of Everything (IoE) poses significant challenges in achieving high-capacity and energy-efficient connectivity in 6G wireless networks. Hovered base stations (HBSs) provide a promising solution for enhancing physical-layer performance; however, their mobility and inefficient transmit power allocation (PA) may increase interference and energy consumption. In this paper, a multi-HBS-based NOMA transmission framework is proposed for downlink 6G networks, where each HBS serves multiple IoE devices. The proposed framework jointly optimizes HBS three-dimensional (3D) trajectory, transmit PA, and dynamic decoding order execution to maximize total sum rate (TSR) and total energy efficiency (TEE) under minimum data-rate constraints. The resulting optimization problem is non-convex due to the coupling among optimization variables and constraints. To efficiently solve this problem, a low-complexity and fast-converging hybrid optimization framework integrating a developed genetic algorithm and modified gray wolf optimization is adopted. Simulation results demonstrate that the proposed framework significantly outperforms existing benchmark schemes, achieving up to 23.6% improvement in TSR and 35.8% improvement in TEE. These results confirm the effectiveness of the proposed joint optimization framework for improving overall network performance.

ABSTRACTFormal risk assessment considers characteristics such as proximity, dose, and vulnerability. However, public risk perception may also be influenced by other—possibly less relevant—factors such as visibility and novelty. The introduction of 5G and its associated infrastructure and radiofrequency electromagnetic fields (RF‐EMF) may therefore change perceptions of RF‐EMF from mobile communications in general. To explore this, we conducted an online survey in 10 European countries (n = 10,358) using a picture‐based approach. Respondents perceived daily RF‐EMF exposures as moderate but expected them to increase with 5G. A mobile phone at the ear was generally associated with higher perceived exposure than multiple base stations. Overall, distance to the RF‐EMF source most strongly influenced perceived exposure, followed by the number of sources. 5G reception was linked to higher exposure perception than 4G or Wi‐Fi reception. These patterns were consistent across most countries. We conclude that when assessing RF‐EMF exposure, people rely on heuristics (e.g., more sources imply more exposure) that often guide them correctly. Understanding when and why people feel particularly exposed can help develop more effective communication about true levels of exposure and risk.

Unmanned aerial vehicle (UAV)-assisted millimeter-wave (mmWave) and terahertz (THz) communications are promising enablers of ultra-reliable and low-latency communication in next-generation wireless networks. However, the initial access and beam alignment process remains challenging because highly directional beams must be rapidly aligned in a three-dimensional environment. In this paper, we investigate a risk-aware beam alignment framework for UAV-assisted mmWave/THz systems, where user equipment scans a 3D spherical region to detect UAV base stations. The objective is to jointly minimize the expected cell-search latency and its variance while satisfying detection-failure and link-quality constraints. To solve this non-convex optimization problem efficiently, we employ the Lévy Self-Renewable Flow Direction Algorithm (LSRFDA), which combines Lévy-flight exploration with self-renewal to improve convergence robustness. A unified propagation model is adopted to cover both mmWave and THz regimes by incorporating free-space spreading loss and frequency-dependent molecular absorption. Extensive Monte Carlo simulations compare the proposed approach with Particle Swarm Optimization, Random Search, Reinforcement Learning, and PPO-Lagrangian methods. The results show that LSRFDA achieves lower latency, lower latency variation, more reliable detection, and lower energy consumption across a wide range of UAV densities and coverage radii. These outcomes highlight the effectiveness of risk-aware geometric optimization for fast and dependable initial access in UAV-assisted 5G mmWave and 6G THz networks.

Grant-free non-orthogonal multiple access (NOMA) enables communication without a scheduling process. Base station (BS) must detect active users without knowing their number, a challenge that also occurs in grant-free NOMA–Internet of Things (IoT). Device detection in grant-free NOMA-IoT can be considered as signal reconstruction in compressive sensing (CS). To address this limitation, we propose extended sparsity estimation- orthogonal matching pursuit (ESE-OMP) to detect active devices in single measurement vector (SMV) and multiple measurement vector (MMV) problems for grant-free NOMA-IoT systems, a reconstruction method in CS that operates without prior knowledge of the sparsity level, which corresponds to the number of active devices. The algorithm iteratively detects active devices by monitoring the absolute difference in -norm of successive residuals, terminating when the change falls below a predefined threshold . ESE-OMP is evaluated under various grant-free NOMA-IoT systems, irregular low-density spreading-orthogonal frequency division multiplexing (LDS-OFDM), regular LDS-OFDM, and pattern division multiple access (PDMA) systems. When the signal-to-noise ratio (SNR) is 10 dB for the SMV problem with static active device composition, the regular LDS-OFDM system achieves a bit error rate (BER) of , while irregular LDS-OFDM and PDMA systems achieve BERs of and , respectively. The smaller the number of active devices, the better the performance of ESE-OMP.

• First systematic study quantifying the synthetic-to-real generalization gap in RF fingerprinting localization. • Novel Gaussian Process calibration method significantly improves alignment between simulated and real base station parameters. • Large-scale synthetic pretraining reduces real-world localization error by 50 • Demonstrated that simulation fidelity outweighs dataset size: calibrated synthetic data outperforms larger uncalibrated datasets. Radio frequency (RF) fingerprinting is a promising localization technique for GPS-denied environments, yet it tends to suffer from a fundamental limitation: Poor generalization to previously unmapped areas. Traditional methods such as k -nearest neighbors ( k -NN) perform well where data is available but may fail on unseen streets, limiting real-world deployment. Deep learning (DL) offers potential remedies by learning spatial-RF patterns that generalize, but requires far more training data than what simple real-world measurement campaigns can provide. In this paper, we investigate whether synthetic data can bridge this generalization gap. Using (i) a real-world dataset from Rome and (ii) NVIDIA’s open-source ray-tracing simulator Sionna, we generate synthetic datasets under varying realism and scale conditions. Specifically, we use Dataset A containing real-world measurements with real base stations (BS) and real signals, and create Dataset B using real BS locations but simulated signals, Dataset C with both simulated BS locations and signals, and Dataset B’ which represents an optimized version of Dataset B where BS parameters are calibrated via Gaussian Process to maximize signal correlation with Dataset A. Our evaluation reveals a pronounced sim-to-real gap: Models achieving 25m error on synthetic data degrade to 184m on real data. Nonetheless, pretraining on synthetic data reduces real-world localization error from 323m to 162m; a 50% improvement over real-only training. Notably, simulation fidelity proves more important than scale: A smaller calibrated dataset (53K samples) outperforms a larger uncalibrated one (274K samples). To further evaluate the generalization capabilities of the models, we conduct experiments on an unseen geographical region using a real-world dataset from Oslo. In the zero-shot setting, the models achieve a root mean square error (RMSE) of 132.2m on the entire dataset, and 61.5m on unseen streets after fine-tuning on Oslo data. While challenges remain before meeting more practical localization accuracy, this work provides a systematic study in the field of wireless communication of synthetic-to-real transfer in RF localization and highlights the value of simulation-aware pretraining for generalizing DL models to real-world scenarios.

Unmanned aerial vehicles for communication recovery in post-disaster scenarios: A PRISMA-based systematic review

In recent years, achieving energy efficiency (EE) has become a critical focus due to the rising demand for environmentally friendly communication systems. This study presents an integrated framework for optimizing base station (BS) deactivation, resource allocation, and renewable energy usage to enhance network efficiency while reducing carbon emissions. A mixed-integer programming model is employed to capture the interdependencies between these factors, and the Artificial Protozoa Optimizer (APO) algorithm is proposed to determine optimal BS deactivation strategies. Additionally, the Lagrange duality method is utilized to optimize power distribution, subcarrier allocation, and energy procurement. Extensive simulations demonstrate that the proposed framework reduces carbon emissions by up to 40 % (achieving as low as 1.8 kg/hour) while simultaneously improving network profit by up to 21 % compared to equal-power allocation baselines. The framework maintains robust performance under realistic inter-cell interference, preserves quality-of-service (coverage >99 % even with 60 % BS sleep ratio), and scales efficiently to dense user loads (up to 50 users per cell). Sensitivity analysis further reveals that the environmental and economic gains are attainable under practical policy regimes, including moderate carbon pricing (≥0.02 $/kg CO₂) and renewable energy subsidies. These findings validate the efficiency, practicality, and policy-awareness of the proposed optimization framework, establishing its potential as a comprehensive solution for sustainable wireless networks. • Increases HC by 14.2 % and 9.57 %, respectively, reducing annual operating costs by 58 % and 11.86 %. • The robust model achieves a 12.4 % higher HC and a 23.7 % lower cost. • It yields a 9.6 % HC gain and an 11.9 % cost saving. • Delivering 41.75 MW HC at an annual cost of $2.72 M.

Hierarchical distributed scheduling of distribution networks using 5G base station clusters with fault-tolerant consensus-based allocation

Fabrication and performance of aluminum heat sink with graded laminated composite wick for communication base stations

Design and implementation of high gain dual-polarized antenna for sub-6GHz applications

CausaLM-Net: An LLM-guided causal graph and state-space learning framework for fault diagnosis in cloud native 5G base stations

Intelligent mining demands real-time processing of UAV sensor streams under latency, safety, and energy constraints. We study a dual-edge architecture in which a ground base station (BS) and an aerial edge server (AES) collaboratively serve aerial users (FDMA), while a protective jammer UAV adapts its trajectory subject to speed/acceleration limits and rotary-wing propulsion. The system models 3GPP A2G channels (probabilistic LoS/NLoS and state-conditioned fading) and passive ground-eavesdroppers with bounded location uncertainty. We formulate a robust energy-efficiency maximization that epigraphs worst-case eavesdropper rates, introduces secrecy-QoS slack variables for feasibility, and enforces slot-level task causality. The fractional objective is handled via Dinkelbach, and three-block BCD solves the problem with conservative SCA surrogates; a micro-AO resolves the bi-convex throughput epigraph in the radio block, and the trajectory block uses affine secrecy bounds plus an SOC treatment of induced power in the rotary-wing model. Under 3GPP Urban Macro calibration, the proposed scheme attains 60.5 kbits/J at [Formula: see text], exceeding STRO by 22.2% and SHJ by 116.1%. With eavesdropper uncertainty [Formula: see text]m, energy efficiency degrades only 13.1%, whereas non-robust CENR collapses to 12.0 kbits/J (76.9% drop). The optimized jammer path is 1250m (vs. 1131m straight line; +10.5%) to secure stronger jamming geometry; propulsion dominates the energy budget at 4800J (86.8% of total), while SLT despite saving 6.3% propulsion energy, incurs a 730.9% increase in RF jamming to 2742J. The algorithm runs in 1.95s/iteration on average (trajectory-frozen: 0.98s/iteration), converges within 5-7 iterations, and captures 85% of its total gain in the first 3 iterations-validating near-real-time feasibility for secure, energy-efficient offloading in mining operations.