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Open Access

Multi-Sample Learning and Density Compensation Particle Swarm Optimization for Base Station Deployment with Special Areas

School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China
School of Mathematical Sciences, Zhejiang Normal University, Jinhua 321004, China
School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
College of Cyber Security, Jinan University, Guangzhou 510632, China
Department of Computer and Information Science, Linköping University, Linköping SE-581 83, Sweden, and also with the Faculty of Science, Fayoum University, Fayoum 63514, Egypt
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Abstract

With the development of the Internet, network communication has been applied to various fields, and people seek higher-speed, lower-latency, more reliable, and more stable signals. Communication between base stations and users is transmitted through radio waves, but wireless signals are affected by various factors, inevitably suffer from loss during propagation. In complex environments, traditional base station deployments often fail to meet users’ various signal requirements. To provide stable and efficient network coverage, deploying base station locations in environments with varying signal demands becomes a challenge. Therefore, this paper designs a complex network environment, including factors such as hot points locations, signal demands, and coverage limitations, to identify optimal base station deployment positions. Precisely because the Particle Swarm Optimization (PSO) algorithm has some unavoidable shortcomings, for example, during the movement of the particles, due to insufficient learning experience, they may become trapped in a local optimum, making it difficult to find new feasible solutions, this paper designs a Multi-Sample Learning and Density Compensation Particle Swarm Optimization (MDPSO) algorithm. By incorporating multi-sample learning and density compensation strategies, MDPSO significantly improves base station coverage. Furthermore, in comparative experiments against four other algorithms, MDPSO demonstrated overall superior performance. Experiments were conducted in two scenarios: fixed hot points and illegal areas locations, and random locations. In both scenarios, the MDPSO algorithm enables model’s regional average coverage to exceed 90%, with the highest average coverage rate among all tested algorithms, and the maximum coverage rate surpassing 95%, validating its effectiveness. This study effectively achieves high network coverage and can provide insights and techniques for research related to base station deployment.

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Complex System Modeling and Simulation
Pages 410-429

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Cite this article:
Wang M, Zhu D, Chen Y, et al. Multi-Sample Learning and Density Compensation Particle Swarm Optimization for Base Station Deployment with Special Areas. Complex System Modeling and Simulation, 2026, 6(4): 410-429. https://doi.org/10.23919/CSMS.2025.0016

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Received: 19 March 2025
Revised: 20 May 2025
Accepted: 04 June 2025
Published: 28 October 2026
© The author(s) 2026.

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/).