Research Output
Articles
Publication Date: 2026
Computer Communications (1873703X)248
The exponential growth in traffic load and increasing number of connected devices have driven cellular networks to offer high capacity and to support massive access. Full-Duplex Ultra-Dense Networks (FD-UDNs) represent a promising technology to meet this demand in cellular networks. However, these networks encounter serious challenges concerning energy consumption and high levels of interference, which, if not properly managed, can adversely affect overall network performance. This paper presents a deep reinforcement learning-based solution for the problem of joint small base station (SBS) on/off switching and resource allocation, with the objective of maximizing energy efficiency and meeting quality of service (QoS) requirements. To reduce complexity, we decompose the problem into two sub-problems: 1) BS sleep management and 2) power and radio resource allocation. For BS sleep management, two approaches are proposed: centralized and distributed. In the centralized approach, the network decides about the sleep state of the SBSs. In the distributed approach, each SBS independently decides on its sleep state. Subsequently, by assigning users to the active stations, each BS allocates transmit power and radio resources to its users. The simulation results highlight performance of the proposed methods compared to the previous method in terms of both energy efficiency and user satisfaction rate. Additionally, the results show that our distributed sleep management method outperforms the centralized one. © 2026
Publication Date: 2025
Computer Communications (1873703X)243
Efficient spectrum utilization is a major challenge in highly dynamic vehicular environments due to the scarcity of spectrum resources. Cognitive Radio (CR) has emerged as a solution to improve spectrum utilization by enabling opportunistic access in IoV. In this context, channel-hopping based blind rendezvous offers a practical approach for decentralized spectrum access in CR-enabled IoV (CR-IoV). This paper presents a novel Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3PG)-based strategy for generating channel sequences in channel-hopping-based blind rendezvous. Unlike existing methods that overlook the quality of licensed spectrum, our approach ensures spectrum efficiency and QoS awareness in dynamic channel sequence generation. We formulate the channel sequence selection problem as a multi-objective optimization, aiming to maximize spectrum efficiency and minimize Time-To-Rendezvous (TTR) while meeting stringent latency and reliability requirements for vehicular communications. Each vehicle independently generates a channel-hopping sequence using a learning agent, which considers key channel quality metrics such as availability, reliability, and capacity. The generated sequences are employed in an asynchronous and asymmetric blind rendezvous process, enhancing adaptability to dynamic network conditions. Simulation results demonstrate that the proposed method significantly outperforms existing approaches, including Enhanced Jump-Stay (EJS), Single-radio Sunflower Set (SSS), Zero-type, One-type, and S-type (ZOS), Multi-Agent Q-Learning based Rendezvous (MAQLR), Exponential-weight algorithm for Exploration and Exploitation (Exp3), and Reinforcement Learning-based Channel-Hopping Rendezvous (RLCH) in terms of Expected TTR (ETTR), Maximum TTR (MTTR), delay, capacity, and reliability. © 2025 Elsevier B.V.
Publication Date: 2025
Journal of Supercomputing (15730484)81(7)
Mobile Crowd Sensing (MCS)-based spectrum monitoring emerges to check the status of the spectrum for dynamic spectrum access. For privacy-preserving purposes, spectrum sensing reports may be sent anonymously. However, anonymous submission of reports increases the probability of fake reports by malicious participants. Also, it is necessary to assign a fair reward to encourage the honest participants, which needs to take into account participant’s reputation. In this research, a method is presented for MCS-based spectrum monitoring which uses Hyperledger Fabric and Identity Mixer (Idemix). This framework overcomes security challenges such as providing anonymity of the participants, identifying malicious participants, detecting intentional and unintentional incorrect reports, and providing a secure protocol to reward participants. An intuitive evaluation of the security features of the proposed method confirms that the proposed method withstands key threats, such as de-anonymization, participant misbehavior, privacy-compromising collusion among system entities, and reputation manipulation attack. Also, numerical evaluations show that the proposed method is superior compared to the similar centralized method in terms of delay when the number of participants is sufficiently large. Specifically, it achieves an average improvement of approximately 39% in scenarios involving 1000 to 2000 participants, and more than a twofold reduction in delay for the case with 2000 participants. Notably, this enhancement comes without a substantial increase in signaling overhead, which remains only slightly more than double that of the centralized method. Moreover, simulations show that the proposed method can successfully distinguish malicious participants from the honest ones in most scenarios. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.
