标题： 在HAPS辅助的6G网络中提高可持续性：负载估计感知的小区切换 显示英文标题

标题： Enhancing Sustainability in HAPS-Assisted 6G Networks: Load Estimation Aware Cell Switching

摘要： 本研究介绍了并解决了垂直异构网络中单元切换中的关键挑战——交通负载估计。单元切换的有效性受到缺乏小型基站（SBS）在睡眠模式下的准确交通负载数据的显著限制，使得许多依赖负载的节能方法变得不切实际，因为它们假设能够完全了解交通负载，而当SBS处于非活动状态时，这种假设是不现实的。换句话说，当SBS处于睡眠模式时，其交通负载无法直接获取，只能进行估计，不可避免地会存在相应的误差。我们并未提出新的切换算法，而是专注于通过探索有效的预测技术来消除这一基础障碍。考虑了一种新型的垂直异构网络模型，将高空平台站（HAPS）作为超级宏基站（SMBS）进行整合。我们研究了空间和时间负载估计方法，包括三种空间插值方案、随机邻近选择、基于距离的选择以及多级聚类（MLC），同时还包括一种基于长短期记忆（LSTM）网络的时间深度学习方法。使用真实世界的数据集进行实证验证，我们的结果表明，空间和时间方法都能显著提高估计精度，其中MLC和LSTM方法表现出特别强大的性能。 显示英文摘要

摘要： This study introduces and addresses the critical challenge of traffic load estimation in cell switching within vertical heterogeneous networks. The effectiveness of cell switching is significantly limited by the lack of accurate traffic load data for small base stations (SBSs) in sleep mode, making many load-dependent energy-saving approaches impractical, as they assume perfect knowledge of traffic loads, an assumption that is unrealistic when SBSs are inactive. In other words, when SBSs are in sleep mode, their traffic loads cannot be directly known and can only be estimated, inevitably with corresponding errors. Rather than proposing a new switching algorithm, we focus on eliminating this foundational barrier by exploring effective prediction techniques. A novel vertical heterogeneous network model is considered, integrating a high-altitude platform station (HAPS) as a super macro base station (SMBS). We investigate both spatial and temporal load estimation approaches, including three spatial interpolation schemes, random neighboring selection, distance based selection, and multi level clustering (MLC), alongside a temporal deep learning method based on long short-term memory (LSTM) networks. Using a real world dataset for empirical validation, our results show that both spatial and temporal methods significantly improve estimation accuracy, with the MLC and LSTM approaches demonstrating particularly strong performance.