Preprint Article Version 1 This version is not peer-reviewed

Performance of Resource Allocation in Device-to-Device Communication Systems Based on Evolutionally Optimization Algorithms

Version 1 : Received: 17 June 2018 / Approved: 20 June 2018 / Online: 20 June 2018 (09:31:54 CEST)

A peer-reviewed article of this Preprint also exists.

Tan, T.-H.; Chen, B.-A.; Huang, Y.-F. Performance of Resource Allocation in Device-to-Device Communication Systems Based on Evolutionally Optimization Algorithms. Appl. Sci. 2018, 8, 1271. Tan, T.-H.; Chen, B.-A.; Huang, Y.-F. Performance of Resource Allocation in Device-to-Device Communication Systems Based on Evolutionally Optimization Algorithms. Appl. Sci. 2018, 8, 1271.

Journal reference: Appl. Sci. 2018, 8, 1271
DOI: 10.3390/app8081271

Abstract

In this study, the resource blocks (RB) are allocated to user equipment (UE) according to the evolutional algorithms for long term evolution (LTE) systems. Particle Swarm Optimization (PSO) algorithm is one of the evolutionary algorithms, based on the imitation of a flock of birds foraging behavior through learning and grouping the best experience. In previous work, the Simple Particle Swarm Optimization (SPSO) algorithm was proposed for RB allocation to enhance the throughput of Device-to-Device (D2D) communications and improve the system capacity performance. In simulation results, with less population size of M = 10, the SPSO can perform quickly convergence to sub-optimal solution in the 100th generation and obtained sub-optimum performance with more 2 UEs than the Rand method. Genetic algorithm (GA) is one of the evolutionary algorithms, based on Darwinian models of natural selection and evolution. Therefore, we further proposed a Refined PSO (RPSO) and a novel GA to enhance the throughput of UEs and to improve the system capacity performance. Simulation results show that the proposed GA with 100 populations, in 200 generations can converge to suboptimal solutions. Therefore, with comparing with the SPSO algorithm the proposed GA and RPSO can improve system capacity performance with 1.8 and 0.4 UEs, respectively.

Subject Areas

device-to-device; LTE systems; resource allocation; particle swarm optimization algorithm; genetic algorithm; system capacity

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