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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.08813 |
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| _version_ | 1866916159486427136 |
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| author | Lin, Hsin Su, Yi-Kang Chen, Hong-Qi Ko, La-Fei |
| author_facet | Lin, Hsin Su, Yi-Kang Chen, Hong-Qi Ko, La-Fei |
| contents | Despite advances in cellular network technology, base station (BS) load balancing remains a persistent problem. Although centralized resource allocation methods can address the load balancing problem, it still remains an NP-hard problem. In this research, we study how federated deep Q learning can be used to inform each user equipment (UE) of the each BS's load conditions. Federated deep Q learning's load balancing enables intelligent UEs to independently select the best BS while also limiting the amount of private information exposed to the network.
In this study, we propose and analyze a federated deep Q learning load balancing system, which is implemented using the Open-RAN xAPP framework and the near-Real Time Radio Interface Controller (near-RT RIC). Our simulation results indicate that compared to the maximum Signal-To-Noise-Ratio (MAX-SINR) method currently used by UEs, our proposed deep Q learning model can consistently provide better High average UE quality of service |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_08813 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Federated Deep Q-Learning and 5G load balancing Lin, Hsin Su, Yi-Kang Chen, Hong-Qi Ko, La-Fei Networking and Internet Architecture Artificial Intelligence Machine Learning Multiagent Systems Despite advances in cellular network technology, base station (BS) load balancing remains a persistent problem. Although centralized resource allocation methods can address the load balancing problem, it still remains an NP-hard problem. In this research, we study how federated deep Q learning can be used to inform each user equipment (UE) of the each BS's load conditions. Federated deep Q learning's load balancing enables intelligent UEs to independently select the best BS while also limiting the amount of private information exposed to the network. In this study, we propose and analyze a federated deep Q learning load balancing system, which is implemented using the Open-RAN xAPP framework and the near-Real Time Radio Interface Controller (near-RT RIC). Our simulation results indicate that compared to the maximum Signal-To-Noise-Ratio (MAX-SINR) method currently used by UEs, our proposed deep Q learning model can consistently provide better High average UE quality of service |
| title | Federated Deep Q-Learning and 5G load balancing |
| topic | Networking and Internet Architecture Artificial Intelligence Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2403.08813 |