A Lightweight Machine Learning Approach for Delay-Aware Cell-Switching in 6G HAPS Networks

This study investigates the integration of a high altitude platform station (HAPS), a non-terrestrial network (NTN) node, into the cell-switching paradigm for energy savings. By doing so, the sustainability and ubiquitous connectivity targets of the sixth generation of communication systems (6G) can be achieved simultaneously. Additionally, a delay-aware approach is also adopted, where the delay profiles of users are respected with a best-effort strategy. To this end, a novel, simple, and lightweight Q-learning algorithm is designed to address the cell-switching optimization problem. Various interference scenarios and delay situations between base stations are examined in terms of energy consumption and quality-of-service (QoS), and the results confirm the efficacy of the proposed algorithm.