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.

WiFi networks have achieved remarkable success in enabling seamless communication and data exchange worldwide. The IEEE 802.11be standard, known as WiFi 7, introduces Multi-Link Operation (MLO), a groundbreaking feature that enables devices to establish multiple simultaneous connections across different bands and channels. While MLO promises substantial improvements in network throughput and latency reduction, it presents significant challenges in channel allocation, particularly in dense network environments. Current research has predominantly focused on performance analysis and throughput optimization within static WiFi 7 network configurations. In contrast, this paper addresses the dynamic channel allocation problem in dense WiFi 7 networks with MLO capabilities. We formulate this challenge as a combinatorial optimization problem, leveraging a novel network performance analysis mechanism. Given the inherent lack of prior network information, we model the problem within a Multi-Armed Bandit (MAB) framework to enable online learning of optimal channel allocations. Our proposed Best-Arm Identification-enabled Monte Carlo Tree Search (BAI-MCTS) algorithm includes rigorous theoretical analysis, providing upper bounds for both sample complexity and error probability. To further reduce sample complexity and enhance generalizability across diverse network scenarios, we put forth LLM-BAI-MCTS, an intelligent algorithm for the dynamic channel allocation problem by integrating the Large Language Model (LLM) into the BAI-MCTS algorithm. Numerical results demonstrate that the BAI-MCTS algorithm achieves a convergence rate approximately 50.44% faster than the state-of-the-art algorithms when reaching 98% of the optimal value. Notably, the convergence rate of the LLM-BAI-MCTS algorithm increases by over 63.32% in dense networks.