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<p>As a robust platform for mobile edge computing, Fifth Generations (5 G) networks, while delivering high data rates and low latency, face a pressing concern with the escalating energy consumption of 5 G Base Stations (BSs). To address the energy efficiency challenges, inspired by the geographical clustering of 5 G users, we propose a heuristic algorithm called <i>User allocation based on Cache, Load, and Distance</i>(U-CLD) which is designed to optimize user allocation based on three key factors that significantly impact BS energy consumption: cache content, workload, and user-to-BS distance. Due to the complexity of the scenarios, heuristic methods are unable to handle the network requirements for all users. Therefore, through an in-depth analysis of the impact weights of various factors on energy consumption in different scenarios, we have proposed a clustering algorithm termed the <i>Environmental Protection Prophet</i> (EPP), which integrates the geographical bias of user request patterns. The EPP algorithm groups users according to their proximity to BSs and utilizes edge caching to reduce response times for user requests. This clustering strategy optimizes user allocation while minimizing BSs’ energy consumption, all while meeting user quality of service requirements. The simulation results demonstrate that the method effectively reduces network operational energy consumption by over 39% and decreases the average user request response time by approximately 12 ms.</p>

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Energy-efficient user allocation and cache updating in mobile edge computing networks based on user geographical aggregation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月4日

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  • Jingchao Tan,
  • Tiancheng Zhang,
  • Cheng Zhang,
  • Chenyang Wang,
  • Xiuhua Li,
  • Xiaofei Wang

摘要

As a robust platform for mobile edge computing, Fifth Generations (5 G) networks, while delivering high data rates and low latency, face a pressing concern with the escalating energy consumption of 5 G Base Stations (BSs). To address the energy efficiency challenges, inspired by the geographical clustering of 5 G users, we propose a heuristic algorithm called User allocation based on Cache, Load, and Distance(U-CLD) which is designed to optimize user allocation based on three key factors that significantly impact BS energy consumption: cache content, workload, and user-to-BS distance. Due to the complexity of the scenarios, heuristic methods are unable to handle the network requirements for all users. Therefore, through an in-depth analysis of the impact weights of various factors on energy consumption in different scenarios, we have proposed a clustering algorithm termed the Environmental Protection Prophet (EPP), which integrates the geographical bias of user request patterns. The EPP algorithm groups users according to their proximity to BSs and utilizes edge caching to reduce response times for user requests. This clustering strategy optimizes user allocation while minimizing BSs’ energy consumption, all while meeting user quality of service requirements. The simulation results demonstrate that the method effectively reduces network operational energy consumption by over 39% and decreases the average user request response time by approximately 12 ms.