Multiobjective optimization based on self‐organizing Particle Swarm Optimization algorithm for massive MIMO 5G wireless network,International Journal of Communication Systems

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Multiobjective optimization based on self‐organizing Particle Swarm Optimization algorithm for massive MIMO 5G wireless network
International Journal of Communication Systems ( IF 1.7 ) Pub Date : 2021-01-03 , DOI: 10.1002/dac.4725
Kesavalu Elumalai Purushothaman ₁ , Velmurugan Nagarajan ₁

Affiliation

The development of future fifth‐generation (5G) wireless networks (WNs) is an active research area worldwide. The 5G network grants significantly upgraded necessities contrasted with those in present wireless systems. Although massive MIMO (mMIMO) incorporation in WN empowers one to encounter 5G network technical necessities, it should handle different challenges to increase the performance. In this paper, a novel multiple objective self‐organizing particle swarm optimizer (SOMPSO) is used to solve multiple objective functions such as user data rate, energy efficiency, spectral efficiency, and average area rate of 5G WN with mMIMO. Furthermore, a fuzzy decision maker is utilized to select a solution vector for getting the best compromising result. Our experimental outputs demonstrate that this SOMPSO is an efficient and promising method to solve multiple objective problems in 5G networks.

中文翻译：

基于自组织粒子群算法的大规模MIMO 5G无线网络多目标优化

未来的第五代（5G）无线网络（WN）的开发是全球活跃的研究领域。与目前的无线系统相比，5G网络可显着提升必需品。尽管将大量MIMO（mMIMO）集成到WN中使人们能够满足5G网络的技术需求，但它应该应对各种挑战以提高性能。在本文中，一种新颖的多目标自组织粒子群优化器（SOMPSO）用于解决多个目标函数，例如用户数据速率，能量效率，频谱效率以及具有mMIMO的5G WN的平均面积率。此外，利用模糊决策器选择解向量以获得最佳的折衷结果。

更新日期：2021-02-03

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