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Open Access

Optimizing Service Efficacy in 5G HetNets: An Adaptive Acceleration PSO Approach

Guowen Li1, Wenbo Hu1, Yang Zhao2( ), Xudong Lu3
School of Management Science and Engineering, Central University of Finance and Economics, Beijing 102206, China
China Center for Internet Economy Research, Central University of Finance and Economics, Beijing 100081, China
School of Software, Shandong University, Jinan 250101, China
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Abstract

The dense deployment of Femto Base Stations (FBS) assisting Macro Base Stations (MBS) in a Heterogeneous Network (HetNet) resolves the coverage issue of 5G signal transmission. However, the imprudent layout of FBSs results in extensive energy consumption and increased signal interference among base stations. Regulating the transmission power of each base station in the HetNets through the main controller or MBS is essential to maximize the power efficiency of the entire HetNets while adhering to the constraints of basic signal throughput and fairness. To address this challenge, this paper proposes an Adaptive Acceleration Particle Swarm Optimization (AA-PSO) algorithm. This algorithm dynamically determines the inertia weight based on each particle’s optimal position and the global optimal position, and introduces the concept of time-varying parameters to control the learning rate, thus managing the search range and convergence speed of the particle swarm. The results demonstrate that the AA-PSO algorithm can efficiently determine the optimal transmission power of each base station in the HetNets, reduce interference between MBS and FBSs, as well as among FBSs, and ultimately improve the service efficacy of the entire network.

Keywords

service efficacy5G HetNetsMacro Base Station (MBS)Femto Base Station (FBS)Particle Swarm Optimization (PSO)

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International Journal of Crowd Science
Volume 8 Issue 4,
September 2024
Pages 168-175
DOI: 10.26599/IJCS.2024.9100024
Cite this article:
Li G, Hu W, Zhao Y, et al. Optimizing Service Efficacy in 5G HetNets: An Adaptive Acceleration PSO Approach. International Journal of Crowd Science, 2024, 8(4): 168-175. https://doi.org/10.26599/IJCS.2024.9100024

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Published: 16 September 2024
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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