Multiobjective optimization based on self‐organizing Particle Swarm Optimization algorithm for massive MIMO 5G wireless network

Abstract

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.

Analysis and Evaluation of Energy Consumption and Carbon Emission Levels of Products Produced by Different Kinds of Equipment Based on Green Development Concept

“…The gray wolf algorithm was proposed by Mirjalili et al in 2014, and was inspired by the natural predation behavior of gray wolf populations. Similarly to other swarm intelligence algorithms, it can be used to solve complex problems in different fields [54,55]. The gray wolf algorithm is a group intelligence optimization method based on the ecological habits of gray wolf predation.…”

Optimization for the reduction of power in sidebands and side lobes of time‐modulated antenna array

“…Based on this, algorithms are developed to solve computational problems. PSO is based on the movement of a group of birds when they are traveling from one place to another 25–32 …”

“…This has led to the application of this algorithm over wide range of optimization problems. This algorithm has been really effective in terms of performance in solving difficult optimization problems, which has led to development of different versions of this algorithm 31–38 . In HSA, the possible solution is termed as harmony which is made up of decision variable.…”

“…But the dynamic range of excitation coefficients is very high to generate the desired radiation pattern, making the feed network complex to fabricate in reality. Due to the development of advanced computation technologies, a wide range of optimization algorithms, like genetic algorithm (GA), 21 differential evolution (DE), 22 simulated annealing, 23,24 and particle swarm optimization (PSO), 25–32 have many applications in the pattern synthesis of the antenna array. In this paper, an analytical approach is taken towards getting a reduced power loss for the antenna arrays.…”

Application of Intelligent Optimization Techniques to Spectral and Energy Efficiencies in Massive MIMO Systems at Different Circuit Power Levels

“…are tried to be optimized in massive MIMO systems [14][15][16][17]. Studies to improve the SE-EE trade-off through intelligent optimizations are known [18][19][20]. There are studies in which the most appropriate values of SE-EE trade-off are determined by accepting some parameters as independent variables for intelligent optimization techniques [21][22].…”