A Lightweight Machine Learning Approach for Delay-Aware Cell-Switching in 6G HAPS Networks
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866929249118584832 |
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| author | Koç, Görkem Berkay Çiloğlu, Berk Ozturk, Metin Yanikomeroglu, Halim |
| author_facet | Koç, Görkem Berkay Çiloğlu, Berk Ozturk, Metin Yanikomeroglu, Halim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_13096 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Lightweight Machine Learning Approach for Delay-Aware Cell-Switching in 6G HAPS Networks Koç, Görkem Berkay Çiloğlu, Berk Ozturk, Metin Yanikomeroglu, Halim Networking and Internet Architecture Signal Processing 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. |
| title | A Lightweight Machine Learning Approach for Delay-Aware Cell-Switching in 6G HAPS Networks |
| topic | Networking and Internet Architecture Signal Processing |
| url | https://arxiv.org/abs/2402.13096 |