Scholarlink

<p>The base station, as a core component of wireless communication systems, provides connectivity between mobile devices and communication networks. The quality of its deployment directly affects the stability of user communications in various environments. This paper establishes a three-dimensional base station communication model to address the overall signal coverage rate in special regions with specific coverage requirements, which involves the deployment of base stations. Consequently, the block-type reinforcement learning (RL) based on the matrix-based particle swarm optimization (BRL-MPSO) algorithm is proposed for this purpose. This method leverages the global search capabilities of the matrix-based particle swarm optimization (MPSO) to improve the Q-table processing in reinforcement learning (RL) through block-type handling, thereby enhancing the efficiency of the algorithm and reducing computational costs. To more accurately reflect the specific coverage requirements of base stations, simulation experiments are performed. After 500 iterations, the overall coverage stabilized at approximately 95.6403%, peaking at 96.6407%, while simultaneously satisfying the specific coverage requirements for special regions. The feasibility and effectiveness of this method under the constraints of special regions are validated through comparative analysis with other algorithms and ablation experiments.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Block-type reinforcement learning based on matrix-based particle swarm optimization for base station deployment

  • 期刊论文

  • 发表日期：

    2025年9月15日

查看全文
  • Yangyang Zheng,
  • Leyi Wang,
  • Jiaying Shen,
  • Xiaoyi Yu,
  • Jialing Hu,
  • Zhaolong Ouyang

摘要

The base station, as a core component of wireless communication systems, provides connectivity between mobile devices and communication networks. The quality of its deployment directly affects the stability of user communications in various environments. This paper establishes a three-dimensional base station communication model to address the overall signal coverage rate in special regions with specific coverage requirements, which involves the deployment of base stations. Consequently, the block-type reinforcement learning (RL) based on the matrix-based particle swarm optimization (BRL-MPSO) algorithm is proposed for this purpose. This method leverages the global search capabilities of the matrix-based particle swarm optimization (MPSO) to improve the Q-table processing in reinforcement learning (RL) through block-type handling, thereby enhancing the efficiency of the algorithm and reducing computational costs. To more accurately reflect the specific coverage requirements of base stations, simulation experiments are performed. After 500 iterations, the overall coverage stabilized at approximately 95.6403%, peaking at 96.6407%, while simultaneously satisfying the specific coverage requirements for special regions. The feasibility and effectiveness of this method under the constraints of special regions are validated through comparative analysis with other algorithms and ablation experiments.