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

2025-06-05

AI Summary

In WiFi 7 dense networks, dynamic multi-link operation (MLO) channel allocation remains challenging under prior-information scarcity. Method: We propose a model-free online learning framework, introducing BAI-MCTS—a novel algorithm integrating best-arm identification (BAI) with Monte Carlo tree search (MCTS)—and further enhancing it via large language model (LLM)-guided generalization to yield the LLM-BAI-MCTS scheduling mechanism. Contribution/Results: Theoretically and empirically, BAI-MCTS achieves 50.44% faster convergence than state-of-the-art (SOTA) methods; LLM-BAI-MCTS further accelerates convergence by 63.32% in dense scenarios while attaining 98% of optimal performance. To our knowledge, this is the first work integrating LLMs into multi-armed bandit (MAB)-driven wireless resource scheduling, significantly improving adaptability, robustness, and cross-scenario generalization capability.

Technology Category

• Machine Learning: Online Learning & Bandits
• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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.

Problem

• Dynamic channel allocation in dense WiFi 7 networks with MLO
• Combinatorial optimization for optimal channel allocation using MAB
• Enhancing convergence and generalizability via LLM-integrated MCTS algorithm

Innovation

• Uses Multi-Armed Bandit for dynamic channel allocation
• Integrates Large Language Model to enhance algorithm
• Proposes BAI-MCTS for faster convergence in networks