Resource allocation based on hybrid genetic algorithm and particle swarm optimization for D2D multicast communications

摘要

Device-to-Device (D2D) multicast communication is a new technology of the fifth-generation networks (5G) for efficiently coping with the ever-increasing demand for content sharing among users. In fact, it enables direct communication between devices in proximity and improves spectral efficiency by reusing a licensed cellular spectrum. The existing related studies show that D2D communications increase network capacity and reduce latency. Nevertheless, the interference management should be carried out in a coordinated manner in order to realize the full potential of this technology and enable its integration into the cellular architecture. We consider the joint uplink subcarrier allocation and power control in D2D underlying cellular networks. In single rate multicast communications, the achieved data rate is greatly limited by the nodes with low channel quality. In this article, we formulate the resource allocation as a max-min optimization problem. Such an optimization problem is in general an NP-hard combinatorial problem and its solution typically requires searching enormous search trees. We propose a multicast schemes based on Genetic Algorithm (GA) and Binary Particle Swarm Optimization (BPSO). We implemented 8 different transfer functions combined with 2 update position strategies in order to assess the performance of BPSO. For the GA, we implemented Uniform and Multipoint crossover methods combined with Roulette Wheel and Tournament selectors. In addition to providing a deep understanding of the algorithm behavior, we present numerical results that demonstrates that GA outperforms BPSO in terms of minimum achieved data rate when the pressure of infeasibility is high. (C) 2019 Elsevier B.V. All rights reserved.