Learning Based Dynamic Cluster Reconfiguration for UAV Mobility Management with 3D Beamforming
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866913470523375616 |
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| author | Meer, Irshad A. Besser, Karl-Ludwig Ozger, Mustafa Schupke, Dominic Poor, H. Vincent Cavdar, Cicek |
| author_facet | Meer, Irshad A. Besser, Karl-Ludwig Ozger, Mustafa Schupke, Dominic Poor, H. Vincent Cavdar, Cicek |
| contents | In modern cell-less wireless networks, mobility management is undergoing a significant transformation, transitioning from single-link handover management to a more adaptable multi-connectivity cluster reconfiguration approach, including often conflicting objectives like energy-efficient power allocation and satisfying varying reliability requirements. In this work, we address the challenge of dynamic clustering and power allocation for unmanned aerial vehicle (UAV) communication in wireless interference networks. Our objective encompasses meeting varying reliability demands, minimizing power consumption, and reducing the frequency of cluster reconfiguration. To achieve these objectives, we introduce a novel approach based on reinforcement learning using a masked soft actor-critic algorithm, specifically tailored for dynamic clustering and power allocation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_00224 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning Based Dynamic Cluster Reconfiguration for UAV Mobility Management with 3D Beamforming Meer, Irshad A. Besser, Karl-Ludwig Ozger, Mustafa Schupke, Dominic Poor, H. Vincent Cavdar, Cicek Information Theory Signal Processing In modern cell-less wireless networks, mobility management is undergoing a significant transformation, transitioning from single-link handover management to a more adaptable multi-connectivity cluster reconfiguration approach, including often conflicting objectives like energy-efficient power allocation and satisfying varying reliability requirements. In this work, we address the challenge of dynamic clustering and power allocation for unmanned aerial vehicle (UAV) communication in wireless interference networks. Our objective encompasses meeting varying reliability demands, minimizing power consumption, and reducing the frequency of cluster reconfiguration. To achieve these objectives, we introduce a novel approach based on reinforcement learning using a masked soft actor-critic algorithm, specifically tailored for dynamic clustering and power allocation. |
| title | Learning Based Dynamic Cluster Reconfiguration for UAV Mobility Management with 3D Beamforming |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2402.00224 |