Genetic Based Approach for Optimal Power and Channel Allocation to Enhance D2D Underlaied Cellular Network Capacity in 5G

Abstract

With the obvious throughput shortage in traditional cellular radio networks, Device-To-Device (D2D) communications has gained a lot of attention to improve the utilization, capacity and channel performance of nextgeneration networks. In this paper, we study a joint consideration of power and channel allocation based on genetic algorithm as a promising direction to expand the overall network capacity for D2D underlaied cellular networks. The genetic based algorithm targets allocating more suitable channels to D2D users and finding the optimal transmit powers for all D2D links and cellular users efficiently, aiming to maximize the overall system throughput of D2D underlaied cellular network with minimum interference level, while satisfying the required quality of service QoS of each user. The simulation results show that our proposed approach has an advantage in terms of maximizing the overall system utilization than fixed, random, BAT algorithm (BA) and Particle Swarm Optimization (PSO) based power allocation schemes.