Telecom, Volume 6, Issue 3 (September 2025) – 18 articles

Achieving ubiquitous coverage in 6G networks presents significant challenges due to the limitations of high-frequency signals and the need for extensive infrastructure, and providing seamless connectivity in remote and rural areas remains a challenge. We propose an integrated optimization framework for UAV-LEO-RIS-assisted wireless networks, aiming to maximize system sum rate through the strategic placement and configuration of Unmanned Aerial Vehicles (UAVs), Low Earth Orbit (LEO) satellites, and Reconfigurable Intelligent Surfaces (RIS). The framework employs a dual wireless backhaul and utilizes a grid search method for UAV placement optimization, ensuring a comprehensive evaluation of potential positions to enhance coverage and data throughput. Simulated Annealing (SA) is utilized for RIS placement optimization, effectively navigating the solution space to identify configurations that improve signal reflection and network performance. For sum rate maximization, we incorporate several metaheuristic algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grey Wolf Optimization (GWO), Salp Swarm Algorithm (SSA), Marine Predators Algorithm (MPA), and a hybrid PSO-GWO approach. Simulation results demonstrate that the hybrid PSO-GWO algorithm outperforms individual metaheuristics in terms of convergence speed and achieving a higher sum rate. The coverage improves from 62% to 100%, and the results show an increase in spectrum efficiency of 23.7%. Full article

Federated Learning (FL) enables privacy-preserving model training across distributed clients. However, its reliance on voluntary client participation makes it vulnerable to strategic behaviors—actions that are not overtly malicious but significantly impair model convergence and fairness. Existing defense methods primarily focus on explicit attacks, overlooking the challenges posed by economically motivated “pseudo-honest” clients. To address this gap, we propose a Reputation-Aware Defense Framework to mitigate strategic behaviors in FL. This framework introduces a multi-dimensional dynamic reputation model that evaluates client behaviors based on gradient alignment, participation consistency, and update stability. The resulting reputation scores are incorporated into both aggregation and incentive mechanisms, forming a behavior-feedback loop that rewards honest participation and penalizes opportunistic strategies. We theoretically prove the convergence of reputation scores, the suppression of low-quality updates in aggregation, and the emergence of honest participation as a Nash equilibrium under the incentive mechanism. Experiments on datasets such as CIFAR-10, FEMNIST, MIMIC-III demonstrate that our approach significantly outperforms baseline methods in accuracy, fairness, and robustness, even when up to 60% of clients act strategically. This study bridges trust modeling and robust optimization in FL, offering a secure foundation for federated systems operating in open and incentive-driven environments. Full article

Efficient bandwidth allocation in 5G networks is essential for optimizing network performance and ensuring high quality of service (QoS), particularly in unmanned aerial vehicle (UAV) communication systems. The dynamic nature of UAV networks presents challenges in managing fluctuating QoS levels, necessitating intelligent bandwidth allocation strategies. This study investigates the effectiveness of two machine learning (ML) models, least square gradient boosting (LSGB) and a Bayesian regularization feedforward neural network (BRFFNN), in predicting bandwidth allocation for UAV ground station (UAV-GS) communication under 5G specifications. Using a simulation-based approach, the study evaluates UAV bandwidth allocation under two movement patterns: circular and random. The QoS metrics considered include the packet delivery ratio (PDR), delay, and throughput. The results demonstrate that the BRFFNN outperforms LSGB, particularly in circular UAV movement, achieving a 100% PDR, a 0.00773 ms delay, and a 3.232 million packets per second (pps) throughput. These findings suggest that ML models, particularly the BRFFNN, can significantly enhance bandwidth allocation strategies in 5G UAV-GS communication systems, improving overall network efficiency and QoS. This study provides valuable insights into ML-driven bandwidth allocation, emphasizing the BRFFNN as a superior approach for enhancing QoS in 5G UAV-GS networks. In the context of 5G UAV-GS bandwidth allocation, this study applies the BRFFNN in a novel way and demonstrates its superiority over tree-based models such as LSGB. In contrast to earlier research that concentrated on static or traditional allocation techniques, our method achieves State-of-the-Art QoS by dynamically predicting bandwidth under actual UAV movement scenarios. Full article

Artificial intelligence (AI) has emerged as a transformative technology in the evolution of massive multiple-input multiple-output (ma-MIMO) systems, positioning them as a cornerstone for sixth-generation (6G) wireless networks. Despite their significant potential, ma-MIMO systems face critical challenges at the receiver end, particularly in signal detection under high-dimensional and noisy environments. To address these limitations, this paper proposes MIMONet, a novel deep learning (DL)-based MIMO detection framework built upon a lightweight and optimized feedforward neural network (FFNN) architecture. MIMONet is specifically designed to achieve a balance between detection performance and complexity by optimizing the neural network architecture for MIMO signal detection tasks. Through extensive simulations across multiple MIMO configurations, the proposed MIMONet detector consistently demonstrates superior bit error rate (BER) performance. It achieves a notably lower error rate compared to conventional benchmark detectors, particularly under moderate to high signal-to-noise ratio (SNR) conditions. In addition to its enhanced detection accuracy, MIMONet maintains significantly reduced computational complexity, highlighting its practical feasibility for advanced wireless communication systems. These results validate the effectiveness of the MIMONet detector in optimizing detection accuracy without imposing excessive processing burdens. Moreover, the architectural flexibility and efficiency of MIMONet lay a solid foundation for future extensions toward large-scale ma-MIMO configurations, paving the way for practical implementations in beyond-5G (B5G) and 6G communication infrastructures. Full article

Free-space optical (FSO) communications have emerged as a promising complement to conventional radio-frequency (RF) systems due to their high bandwidth, low interference, and license-free spectrum. Visible-light FSO communication, using laser diodes or LEDs, offers potential for short-range data links, but performance is highly wavelength-dependent under varying atmospheric conditions. This study presents an experimental evaluation of three visible laser diodes at 650 nm (red), 532 nm (green), and 405 nm (violet), focusing on their optical output power, quantum efficiency, and modulation behavior across a range of driving currents and frequencies. A custom laboratory testbed was developed using an Atmega328p microcontroller and a Visual Basic control interface, allowing precise control of current and modulation frequency. A silicon photovoltaic cell was employed as the optical receiver and energy harvester. The results demonstrate that the 650 nm red laser consistently delivers the highest quantum efficiency and optical output, with stable performance across electrical and modulation parameters. These findings support the selection of 650 nm as the most energy-efficient and versatile wavelength for short-range, cost-effective visible-light FSO communication. This work provides experimentally grounded insights to guide wavelength selection in the development of energy-efficient optical wireless systems. Full article

This study presents a semi-random mobility model called Semi-Random Elliptical Movement (SREM), developed for relay-oriented Flying Ad Hoc Networks (FANETs). In FANETs, node distribution has a major impact on network performance, making the mobility model a critical design element. While random models offer simplicity and path diversity, they often result in unstable relay paths due to inconsistent node placement. In contrast, planned path models provide alignment but lack the flexibility needed in dynamic environments. SREM addresses these challenges by enabling nodes to move along elliptical trajectories, combining autonomous movement with alignment to the relay path. This approach encourages natural node concentration along the relay path while maintaining distributed mobility. The spatial characteristics of SREM have been analytically defined and validated through the Monte Carlo method, confirming stable node distributions that support effective relaying. Computer simulation results show that SREM performs better than general mobility models that do not account for relaying, offering more suitable performance in relay-focused scenarios. These findings suggest that SREM provides both structural consistency and practical effectiveness, making it a strong candidate for improving the realism and reliability of FANET simulations involving relay-based communication. Full article

Opportunistic Networks (OppNets) lack the capability to maintain consistent end-to-end paths between source and destination nodes, unlike Mobile Ad Hoc Networks (MANETs). This absence of stable routing presents substantial challenges for data transmission in OppNets. Due to node mobility, routing paths are inherently dynamic, requiring the selection of neighboring nodes as intermediate hops to forward data toward the destination. However, frequent node movement can cause considerable delays for senders attempting to identify appropriate next hops, consequently degrading the quality of service (QoS) in OppNets. To mitigate this challenge, this paper proposes an alternative approach for scenarios where senders cannot locate suitable next hops. Specifically, we propose utilizing direct communication via line of sight (LoS) between sender and receiver nodes to satisfy QoS requirements. The proposed scheme is experimented with using the ONE simulator, which is widely used for OppNet experiments and study, and compared against existing schemes such as the history-based routing protocol (HBRP) and AEProphet routing protocol. Full article

As the electromagnetic spectrum is a limited and valuable resource, auctions have emerged as an effective tool for promoting efficient allocation and generating revenue. This article proposes a theoretical review of the most commonly used auction formats for spectrum auctions, highlighting the primary strengths and weaknesses of each format. Additionally, comparisons are made between the revenue generated by different auction formats and the corresponding countries in North and South America during the 21st century. The conclusion drawn is that the Combinatorial Clock Auction format is the preferred choice, as it consistently leads to more efficient allocation, as measured by the revenue generated from each auction. Full article

The swift advancement of fifth-generation (5G) wireless technology brings forth a range of enhancements to address the increasing demand for data, the proliferation of smart devices, and the growth of the Internet of Things (IoT). This highly interconnected communication environment necessitates using multiple-input multiple-output (MIMO) systems to achieve adequate channel capacity. In this article, a 2-port MIMO system using two flipped parallel 1 × 2 arrays and a 2-port MIMO system using two opposite 1 × 4 arrays designed and fabricated antennas for 5G wireless communication in the sub-6 GHz band, are presented, overcoming the limitations of previous designs in gain, radiation efficiency and MIMO performance. The designed and fabricated single-element antenna features a circular microstrip patch design based on ROGER 5880 (RT5880) substrate, which has a thickness of 1.57 mm, a permittivity of 2.2, and a tangential loss of 0.0009. The 2-port MIMO of two 1 × 2 arrays and the 2-port MIMO of two 1 × 4 arrays have overall dimensions of 132 × 66 × 1.57 mm³ and 140 × 132 × 1.57 mm³, respectively. The MIMO of two 1 × 2 arrays and MIMO of two 1 × 4 arrays encompass maximum gains of 8.3 dBi and 10.9 dBi, respectively, with maximum radiation efficiency reaching 95% and 97.46%. High MIMO performance outcomes are observed for both the MIMO of two 1 × 2 arrays and the MIMO of two 1 × 4 arrays, with the channel capacity loss (CCL) ˂ 0.4 bit/s/Hz and ˂0.3 bit/s/Hz, respectively, an envelope correlation coefficient (ECC) ˂ 0.006 and ˂0.003, respectively, directivity gain (DG) about 10 dB, and a total active reflection coefficient (TARC) under −10 dB, ensuring impedance matching and effective mutual coupling among neighboring parameters, which confirms their effectiveness for 5G applications. The three fabricated antennas were experimentally tested and implemented using the MIMO Application Framework version 19.5 for 5G systems, demonstrating operational effectiveness in 5G applications. Full article

This study presents an in-depth dosimetry analysis of energy assimilation from EM waves and increase in the temperature during mobile phone usage within an elevator cabin. The cellphone operates at two different frequencies (1000 MHz and 1800 MHz) and is simulated at three different talk positions vertical, tilt, and cheek. Realistic numerical models of a woman in the third trimester of pregnancy and a girl at the age of 5 years are employed. The analysis highlights the necessity of a comprehensive approach to fully grasp the complexities of EM exposure. Full article

Over-the-air federated learning (Air-FL) has emerged as a promising paradigm that integrates communication and learning, which offers significant potential to enhance model training efficiency and optimize communication resource utilization. This paper addresses the challenge of interference management in multi-cell Air-FL systems, focusing on parallel multi-task scenarios where each cell independently executes distinct training tasks. We begin by analyzing the impact of aggregation errors on local model performance within each cell, aiming to minimize the cumulative optimality gap across all cells. To this end, we formulate an optimization framework that jointly optimizes device transmit power and denoising factors. Leveraging the Pareto boundary theory, we design a centralized optimization scheme that characterizes the trade-offs in system performance. Building upon this, we propose a distributed power control optimization scheme based on interference temperature (IT). This approach decomposes the globally coupled problem into locally solvable subproblems, thereby enabling each cell to adjust its transmit power independently using only local channel state information (CSI). To tackle the non-convexity inherent in these subproblems, we first transform them into convex problems and then develop an analytical solution framework grounded in Lagrangian duality theory. Coupled with a dynamic IT update mechanism, our method iteratively approximates the Pareto optimal boundary. The simulation results demonstrate that the proposed scheme outperforms baseline methods in terms of training convergence speed, cross-cell performance balance, and test accuracy. Moreover, it achieves stable convergence within a limited number of iterations, which validates its practicality and effectiveness in multi-task edge intelligence systems. Full article

Quantum money represents an innovative approach to currency by encoding economic value within the quantum states of physical systems, utilizing the principles of quantum mechanics to enhance security, integrity, and transferability. This perspective article explores the definition and properties of quantum money. We analyze the process of transferring quantum money via quantum teleportation, using terrestrial and satellite-based quantum networks. Furthermore, we consider the impact of quantum money on the modern banking system, particularly in money creation. Finally, we conduct an analysis to assess the strengths and weaknesses of quantum money, as well as opportunities and threats associated with this emerging concept. Full article

To ensure the on-orbit safety of crewed spacecraft and avoid the threat of constellations such as Starlink to manned spacecraft, the industry has started to research equipping phased array radars for situational awareness of collision threat. In order to enhance the resource allocation capability of the space station’s protection radar system, this paper proposes a task scheduling method based on time shifting constraints and pulse interleaving. The time shifting constraint is designed to minimize the deviation between the actual execution and the desired execution time of the task, and it is negatively correlated with the threat degree of the target. Pulse interleaving is intended to utilize the idle time between the transmitted pulse and the received pulse of a task to perform other tasks, thereby improving the utilization of radar resources. Through computer simulation under typical parameters, our proposed method reduces the average time shifting ratio by about 60% compared to traditional task scheduling methods, and the scheduling success ratio is also higher than that of traditional scheduling methods. This demonstrates the effectiveness of the proposed method in enhancing scheduling efficiency and overall system performance. Full article

The Controller Area Network (CAN) protocol is one of the important communication standards in autonomous vehicles, enabling real-time information sharing across in-vehicle (IV) components to realize smooth coordination and dependability in vital activities. Without encryption and authentication, CAN reveals several vulnerabilities related to message attacks within the IV Network (IVN). Traditional centralized Intrusion Detection Systems (IDS) where all the historical data is grouped on one node result in privacy risks and scalability issues, making them unsuitable for real-time intrusion detection. To address these challenges, we propose a Deep Federated Learning (FL) architecture for intrusion detection in IVN. We propose a Bidirectional Long Short Term Memory (BiLSTM) architecture to capture temporal dependencies in the CAN bus and ensure enhanced feature extraction and multi-class classification. By evaluating our framework on three real-world datasets, we show how our proposal outperforms a baseline LSTM model from the state of the art. Full article

Fifth-generation Vehicle-to-Everything (V2X) networks have ushered in a new set of challenges that negatively affect seamless connectivity, specifically owing to high user equipment (UE) mobility and high density. As UE accelerates, there are frequent transitions from one cell to another, and handovers (HOs) are triggered by network performance metrics, including latency, higher energy consumption, and greater packet loss. Traditional HO mechanisms fail to handle such network conditions, requiring the development of Intelligent HO Decisions for V2X (IHD-V2X). By leveraging Q-Learning, the intelligent mechanism seamlessly adapts to real-time network congestion and varying UE speeds, thereby resulting in efficient handover decisions. Based on the results, IHD-V2X significantly outperforms the other mechanisms in high-density and high-mobility networks. This results in a reduction of 73% in unnecessary handover operations, and an 18% reduction in effective energy consumption. On the other hand, it improved handover success rates by 80% from the necessary handover and lowered packet loss for high mobility UE by 73%. The latency was kept at a minimum of 22% for application-specific requirements. The proposed intelligent approach is particularly effective for high-mobility situations and ultra-dense networks, where excessive handovers can degrade user experience. Full article

The rapid growth of high-quality telecommunications demands enhanced queueing system performance. Traditional bandwidth distribution often struggles to adapt to dynamic changes, network conditions, and erratic traffic patterns. Internet traffic fluctuates over time, causing resource underutilization. To address these challenges, this paper proposes a new adaptive algorithm called Weighted Fair Queues continual Deep Reinforcement Learning (WFQ continual-DRL), which integrates the advanced deep reinforcement learning Soft Actor-Critic (SAC) algorithm with the Elastic Weight Consolidation (EWC) approach. This technique is designed to overcome neural networks’ catastrophic forgetting, thereby enhancing network routers’ dynamic bandwidth allocation. The agent is trained to allocate bandwidth weights for multiple queues dynamically by interacting with the environment to observe queue lengths. The performance of the proposed adaptive algorithm was evaluated for eight queues until it expanded to twelve-queue systems. The model achieved higher cumulative rewards as compared to previous studies, indicating improved overall performance. The values of the Mean Squared Error (MSE) and Mean Absolute Error (MAE) decreased, suggesting effectively optimized bandwidth allocation. Reducing Root Mean Square Error (RMSE) indicated improved prediction accuracy and enhanced fairness computed by Jain’s index. The proposed algorithm was validated by employing real-world network traffic data, ensuring a robust model under dynamic queuing requirements. Full article

The increasing demand for sustainable and renewable energy solutions has made radio frequency energy harvesting (RFEH) a promising technique for powering low-power electronic devices. RFEH captures ambient RF signals from wireless communication systems, such as mobile networks, Wi-Fi, and broadcasting stations, and converts them into usable electrical energy. This approach offers a viable alternative for battery-dependent and hard-to-recharge applications, including streetlights, outdoor night/security lighting, wireless sensor networks, and biomedical body sensor networks. This article provides a comprehensive review of the RFEH techniques, including state-of-the-art rectenna designs, energy conversion efficiency improvements, and multi-band harvesting systems. We present a detailed analysis of recent advancements in RFEH circuits, impedance matching techniques, and integration with emerging technologies such as the Internet of Things (IoT), 5G, and wireless power transfer (WPT). Additionally, this review identifies existing challenges, including low conversion efficiency, unpredictable energy availability, and design limitations for small-scale and embedded systems. A critical assessment of current research gaps is provided, highlighting areas where further development is required to enhance performance and scalability. Finally, constructive recommendations for future opportunities in RFEH are discussed, focusing on advanced materials, AI-driven adaptive harvesting systems, hybrid energy-harvesting techniques, and novel antenna–rectifier architectures. The insights from this study will serve as a valuable resource for researchers and engineers working towards the realization of self-sustaining, battery-free electronic systems. Full article

This study provides a comprehensive techno-economic evaluation of the implementation of the 5G network, focusing on the southern region of Brazil. The research examines the capital expenditure (CAPEX) and operational expenditure (OPEX) associated with 5G deployment, assessing the economic viability of various deployment strategies. By analyzing international practices, such as sharing infrastructure, cutting networks, and using neutral networks, this study presents a detailed cost analysis and proposes models to optimize investment. A comparative evaluation of deployment costs between the southern region of Brazil and Belgium underscores the need to adapt European cost models to the Brazilian context. In addition, a case study on rural areas in southern Brazil identifies key challenges and opportunities, highlighting the unique aspects of the implementation of 5G in these regions. This study offers insights into optimizing investments in 5G networks, with the objective of supporting informed decision making for network expansion in diverse geographical and economic contexts. Full article