Intelligent Channel Allocation for IEEE 802.11be Multi-Link Operation: When MAB Meets LLM

TL;DR

This paper introduces a novel intelligent algorithm combining Multi-Armed Bandit models and Large Language Models to optimize dynamic channel allocation in WiFi 7 networks with Multi-Link Operation, significantly improving convergence speed in dense environments.

Contribution

It proposes the LLM-BAI-MCTS algorithm that integrates LLMs into a BAI-MCTS framework for efficient, adaptive channel allocation in WiFi 7, with rigorous theoretical analysis and superior performance.

Findings

BAI-MCTS converges 50.44% faster than existing algorithms.

LLM-BAI-MCTS improves convergence rate by over 63.32% in dense networks.

The proposed methods effectively optimize channel allocation in dynamic WiFi 7 environments.

Abstract

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.