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

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