Dynamic Channel Allocation via Bandit Learning for WiFi 7 Networks with Multi-Link Operation

The upcoming IEEE 802.11be standard, termed WiFi 7, introduces multi-link operation (MLO), enabling devices to establish multiple simultaneous connections utilizing different frequencies and channels. While MLO has the potential to boost network throughput, optimizing channel allocation in WiFi 7 networks introduces many challenges. In this paper, we propose a best-arm identification-enabled Monte Carlo tree search (BAI-MCTS) algorithm for efficient channel allocation in WiFi 7 networks. Specifically, we first employ an efficient mechanism to calculate the network throughput by capturing the essential features of the CSMA protocol. We then formulate this channel allocation problem as a multi-armed bandit (MAB) problem. However, solving this MAB problem induces high sample complexity due to the large-arm space. To overcome this challenge, we introduce BAI-MCTS by combining the BAI and MCTS techniques. Notably, BAI-MCTS has a fast convergence rate and low sample complexity. Simulation results demonstrate that the proposed algorithm outperforms the baseline algorithms in terms of the convergence rate, which is about 42.40% faster than the UCT algorithm when reaching 95% of the optimal value.