Citation

Al-Quhali, Abdullah H. and Roslee, Mardeni and Abuajwa, Osama and Alias, Mohamad Y. (2025) Multi-objective PSO-based dynamic distance-aware resource allocation with adaptive power control for device-to-device (D2D) communications. Discover Applied Sciences, 8 (67). ISSN 3004-9261

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Abstract

In 5G and beyond networks, device-to-device (D2D) communication has become a crucial method for improving spectrum efficiency, reducing latency, and offloading cellular infrastructure due to the exponential growth in data traffic and user density. This paper presents a particle swarm optimization (PSO)-based multi-objective enhanced dynamic resource allocation (PSO-MO-EDRA) scheme for D2D communication underlaying cellular networks. The proposed framework jointly optimizes four key performance metrics: sum rate, outage probability, spectrum utilization, and fairness. To achieve this, PSO-MO-EDRA integrates SINR-aware resource initialization, distance-based adaptive power control, and a multi-objective fitness function with penalty constraints to guarantee minimum user connectivity and QoS. The system model incorporates realistic elements, including user mobility (Random Waypoint), Rayleigh fading, background and multi-cell interference, and cellular user interference, with SINR thresholds based on 3GPP standards. Simulation results show that PSO-MO-EDRA outperforms benchmark methods, achieving 50% higher sum rate, 75% lower outage probability, 15% higher spectrum utilization, and 20% improved fairness. These outcomes validate PSO-MO-EDRA as an efficient, scalable, and low-complexity solution for QoS-aware resource management in dense D2D-enabled wireless networks.

Item Type:	Article
Uncontrolled Keywords:	D2D communication, Resource allocation, PSO, Multi-objective optimization, Fairness, Spectrum utilization, Outage probability, Sum rate
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	11 Feb 2026 00:30
Last Modified:	11 Feb 2026 00:30
URII:	http://shdl.mmu.edu.my/id/eprint/15326

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