This study aims to introduce the cell load estimation problem of cell
switching approaches in cellular networks specially-presented in a
high-altitude platform station (HAPS)-assisted network. The problem arises from
the fact that the traffic loads of sleeping base stations for the next time
slot cannot be perfectly known, but they can rather be estimated, and any
estimation error could result in divergence from the optimal decision, which
subsequently affects the performance of energy efficiency. The traffic loads of
the sleeping base stations for the next time slot are required because the
switching decisions are made proactively in the current time slot. Two
different Q-learning algorithms are developed; one is full-scale, focusing
solely on the performance, while the other one is lightweight and addresses the
computational cost. Results confirm that the estimation error is capable of
changing cell switching decisions that yields performance divergence compared
to no-error scenarios. Moreover, the developed Q-learning algorithms perform
well since an insignificant difference (i.e., 0.3%) is observed between them
and the optimum algorithm.

通过引入 HAPS 辅助网络中的基站切换方法中的小区负载估计问题，本研究旨在解决基站切换决策中的负载估计问题，以提高能量效率。研究表明，估计误差可以改变切换决策，并导致性能差异。此外，开发的 Q 学习算法表现良好，与最优算法之间仅有微小差异（0.3%）。