Interference Management Based on Meta-Heuristic Algorithms in 5G Device-to-Device Communications

Device-to-Device (D2D) communication is an emerging technology that is vital for the future of cellular networks, including 5G and beyond. Its potential lies in enhancing system throughput, offloading the network core, and improving spectral efficiency. Therefore, optimizing resource and power allocation to reduce co-channel interference is crucial for harnessing these benefits. In this paper, we conduct a comparative study of meta-heuristic algorithms, employing Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Bee Life Algorithm (BLA), and a novel combination of matching techniques with BLA for joint channel and power allocation optimization. The simulation results highlight the effectiveness of bio-inspired algorithms in addressing these challenges. Moreover, the proposed amalgamation of the matching algorithm with BLA outperforms other meta-heuristic algorithms, namely, PSO, BLA, and GA, in terms of throughput, convergence speed, and achieving practical solutions.

• device-to-device communication
• 5G
• co-channel interference
• interference management
• metaheuristic algorithms
• genetic algorithm
• particle swarm optimization
• matching algorithms
• throughput
• bio-inspired optimization

• 9 — Industry, innovation and infrastructure

• Advanced MIMO Systems Optimization
• Wireless Communication Networks Research
• Metaheuristic Optimization Algorithms Research
• device-to-device communication
• 5G
• co-channel interference
• interference management
• metaheuristic algorithms
• genetic algorithm
• particle swarm optimization
• matching algorithms
• Electrical and Electronic Engineering
• Computer Networks and Communications
• Artificial Intelligence
• Engineering
• Computer Science