Scholarlink

<p>Sustainability has become a defining requirement for sixth-generation&#xa0;(6G) cellular networks due to the continuously rising density of base station&#xa0;(BS) deployments. Existing studies, including cell switching&#xa0;(CS), renewable energy integration, and non-terrestrial networks in isolation, result in only partial and insufficient solutions. This study introduces a unified optimization framework that jointly optimizes which small BSs&#xa0;(SBSs) should be equipped with solar panels&#xa0;(SPs) and which SBSs should be placed into sleep mode in order to support sustainable 6G networking in terms of energy efficiency. To support the CS mechanism, high-altitude platform stations&#xa0;(HAPS) is employed to enhance capacity and increase switching flexibility. Additionally, a genetic algorithm&#xa0;(GA)-based optimization technique is proposed to jointly determine optimal SP placement and CS configuration. To manage complexity and improve scalability, a <i>k</i>-means clustering method is also used to categorize SBSs according to their traffic characteristics. Simulation results demonstrate that the proposed integrated green energy strategy, leveraging GA and <i>k</i>-means clustering, achieves considerable energy savings compared to baseline approaches. Furthermore, clustering-based problem simplification is shown to deliver performance comparable to the full-scale solution that explores the entire search space.</p>

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Unified Optimization of Solar-Powered Cell Switching in HAPS-Assisted 6G Networks

  • 期刊论文

  • 发表日期：

    2026年7月22日

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  • Metin Ozturk

摘要

Sustainability has become a defining requirement for sixth-generation (6G) cellular networks due to the continuously rising density of base station (BS) deployments. Existing studies, including cell switching (CS), renewable energy integration, and non-terrestrial networks in isolation, result in only partial and insufficient solutions. This study introduces a unified optimization framework that jointly optimizes which small BSs (SBSs) should be equipped with solar panels (SPs) and which SBSs should be placed into sleep mode in order to support sustainable 6G networking in terms of energy efficiency. To support the CS mechanism, high-altitude platform stations (HAPS) is employed to enhance capacity and increase switching flexibility. Additionally, a genetic algorithm (GA)-based optimization technique is proposed to jointly determine optimal SP placement and CS configuration. To manage complexity and improve scalability, a k-means clustering method is also used to categorize SBSs according to their traffic characteristics. Simulation results demonstrate that the proposed integrated green energy strategy, leveraging GA and k-means clustering, achieves considerable energy savings compared to baseline approaches. Furthermore, clustering-based problem simplification is shown to deliver performance comparable to the full-scale solution that explores the entire search space.