A Lightweight Machine Learning Approach for Delay-Aware Cell-Switching in 6G HAPS Networks
59th Annual IEEE International Conference on Communications Workshops, ICC Workshops 2024, Colorado, Amerika Birleşik Devletleri, 9 - 13 Haziran 2024, ss.1517-1522, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/iccworkshops59551.2024.10615903
- Basıldığı Şehir: Colorado
- Basıldığı Ülke: Amerika Birleşik Devletleri
- Sayfa Sayıları: ss.1517-1522
- Anahtar Kelimeler: 6G, cell-switching, green cellular networks, HAPS, HRLLC, sustainability
- Ankara Yıldırım Beyazıt Üniversitesi Adresli: Evet
Özet
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.