A Lightweight Machine Learning Approach for Delay-Aware Cell-Switching in 6G HAPS Networks
<|reference_start|>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 saving. By doing so, the sustainability and ubiquitous connectivity targets can be achieved. Besides, a delay-aware approach is also adopted, where the delay profiles of users are respected in such a way that we attempt to meet the latency requirements of users 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. During the simulation campaigns, different 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 Q-learning algorithm.<|reference_end|>
@article{koç2024a,
    title={A Lightweight Machine Learning Approach for Delay-Aware Cell-Switching
  in 6G HAPS Networks},
    author={G"orkem Berkay Koc{c}, Berk c{C}ilou{g}lu, Metin Ozturk, Halim
  Yanikomeroglu},
    journal={arXiv preprint arXiv:2402.13096},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.13096},
    primaryClass={cs.NI eess.SP}
}