Coordinated Multi-Armed Bandits for Improved Spatial Reuse in Wi-Fi
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910858989273088 |
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| author | Wilhelmi, Francesc Bellalta, Boris Szott, Szymon Kosek-Szott, Katarzyna Barrachina-Muñoz, Sergio |
| author_facet | Wilhelmi, Francesc Bellalta, Boris Szott, Szymon Kosek-Szott, Katarzyna Barrachina-Muñoz, Sergio |
| contents | Multi-Access Point Coordination (MAPC) and Artificial Intelligence and Machine Learning (AI/ML) are expected to be key features in future Wi-Fi, such as the forthcoming IEEE 802.11bn (Wi-Fi~8) and beyond. In this paper, we explore a coordinated solution based on online learning to drive the optimization of Spatial Reuse (SR), a method that allows multiple devices to perform simultaneous transmissions by controlling interference through Packet Detect (PD) adjustment and transmit power control. In particular, we focus on a Multi-Agent Multi-Armed Bandit (MA-MAB) setting, where multiple decision-making agents concurrently configure SR parameters from coexisting networks by leveraging the MAPC framework, and study various algorithms and reward-sharing mechanisms. We evaluate different MA-MAB implementations using Komondor, a well-adopted Wi-Fi simulator, and demonstrate that AI-native SR enabled by coordinated MABs can improve the network performance over current Wi-Fi operation: mean throughput increases by 15%, fairness is improved by increasing the minimum throughput across the network by 210%, while the maximum access delay is kept below 3 ms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_03076 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Coordinated Multi-Armed Bandits for Improved Spatial Reuse in Wi-Fi Wilhelmi, Francesc Bellalta, Boris Szott, Szymon Kosek-Szott, Katarzyna Barrachina-Muñoz, Sergio Networking and Internet Architecture Artificial Intelligence Multi-Access Point Coordination (MAPC) and Artificial Intelligence and Machine Learning (AI/ML) are expected to be key features in future Wi-Fi, such as the forthcoming IEEE 802.11bn (Wi-Fi~8) and beyond. In this paper, we explore a coordinated solution based on online learning to drive the optimization of Spatial Reuse (SR), a method that allows multiple devices to perform simultaneous transmissions by controlling interference through Packet Detect (PD) adjustment and transmit power control. In particular, we focus on a Multi-Agent Multi-Armed Bandit (MA-MAB) setting, where multiple decision-making agents concurrently configure SR parameters from coexisting networks by leveraging the MAPC framework, and study various algorithms and reward-sharing mechanisms. We evaluate different MA-MAB implementations using Komondor, a well-adopted Wi-Fi simulator, and demonstrate that AI-native SR enabled by coordinated MABs can improve the network performance over current Wi-Fi operation: mean throughput increases by 15%, fairness is improved by increasing the minimum throughput across the network by 210%, while the maximum access delay is kept below 3 ms. |
| title | Coordinated Multi-Armed Bandits for Improved Spatial Reuse in Wi-Fi |
| topic | Networking and Internet Architecture Artificial Intelligence |
| url | https://arxiv.org/abs/2412.03076 |