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Article

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

1
Department of Electrical Engineering, National Taipei University of Technology, Taipei City 106, Taiwan
2
Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung City 413, Taiwan
*
Author to whom correspondence should be addressed.
This paper is an extended version of our 2 papers published in 1. 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC2017), pp. 5–8, Banff, AB, Canada, 5–8 October 2017. and 2. International Conference on Genetic and Evolutionary Computing (ICGEC 2017): Genetic and Evolutionary Computing, Advances in Intelligent Systems and Computing book series (AISC), Vol. 579, pp. 26–33, Springer-Verlag: Berlin/Heidelberg, 6–8 November 2017.
Appl. Sci. 2018, 8(8), 1271; https://doi.org/10.3390/app8081271
Received: 17 June 2018 / Revised: 23 July 2018 / Accepted: 27 July 2018 / Published: 31 July 2018
(This article belongs to the Special Issue Selected Papers from IEEE ICASI 2018)
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 these evolutionary algorithms, which imitates the foraging behavior of a flock of birds through learning and grouping the best experience. In previous works, 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. Genetic algorithm (GA) is another evolutionary algorithm, which is based on the 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 can converge to suboptimal solutions in 200 generations. The proposed GA and RPSO can improve system capacity performance compared to SPSO by 2.0 and 0.6 UEs, respectively. View Full-Text
Keywords: device-to-device; LTE systems; resource allocation; particle swarm optimization algorithm; genetic algorithm; system capacity device-to-device; LTE systems; resource allocation; particle swarm optimization algorithm; genetic algorithm; system capacity
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MDPI and ACS Style

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. https://doi.org/10.3390/app8081271

AMA Style

Tan T-H, Chen B-A, Huang Y-F. Performance of Resource Allocation in Device-to-Device Communication Systems Based on Evolutionally Optimization Algorithms . Applied Sciences. 2018; 8(8):1271. https://doi.org/10.3390/app8081271

Chicago/Turabian Style

Tan, Tan-Hsu, Bor-An Chen, and Yung-Fa Huang. 2018. "Performance of Resource Allocation in Device-to-Device Communication Systems Based on Evolutionally Optimization Algorithms " Applied Sciences 8, no. 8: 1271. https://doi.org/10.3390/app8081271

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