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

arXiv: https://arxiv.org/abs/2506.04594 · HTML: https://mlsanity.com/paper/2506.04594/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 44 references that are in the map.
- First posted on arXiv 5 Jun 2025.

## Builds on (most cited first, 15 of 15)

- [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019)
- [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018)
- [Learning Combinatorial Optimization on Graphs: A Survey with Applications to Networking](https://mlsanity.com/paper/2005.11081/) (2020)
- [WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence](https://mlsanity.com/paper/2405.17053/) (2024)
- [Introspective Tips: Large Language Model for In-Context Decision Making](https://mlsanity.com/paper/2305.11598/) (2023)
- [Solving General Natural-Language-Description Optimization Problems with Large Language Models](https://mlsanity.com/paper/2407.07924/) (2024)
- [Large Language Model (LLM)-enabled In-context Learning for Wireless Network Optimization: A Case Study of Power Control](https://mlsanity.com/paper/2408.00214/) (2024)
- [Combinatorial Multivariant Multi-Armed Bandits with Applications to Episodic Reinforcement Learning and Beyond](https://mlsanity.com/paper/2406.01386/) (2024)
- [Best-Arm Identification in Unimodal Bandits](https://mlsanity.com/paper/2411.01898/) (2024)
- [Channel Selection for Wi-Fi 7 Multi-Link Operation via Optimistic-Weighted VDN and Parallel Transfer Reinforcement Learning](https://mlsanity.com/paper/2307.05419/) (2023)
- [An Experimental Study of Latency for IEEE 802.11be Multi-link Operation](https://mlsanity.com/paper/2111.09281/) (2021)
- [Multi-link Operation in IEEE 802.11be WLANs](https://mlsanity.com/paper/2201.07499/) (2022)
- [Delay Analysis of IEEE 802.11be Multi-link Operation under Finite Load](https://mlsanity.com/paper/2212.12420/) (2022)
- [A Federated Reinforcement Learning Framework for Link Activation in Multi-link Wi-Fi Networks](https://mlsanity.com/paper/2304.14720/) (2023)
- [Wi-Fi Multi-Link Operation: An Experimental Study of Latency and Throughput](https://mlsanity.com/paper/2305.02052/) (2023)

## Built on by (most cited first, 1 of 1)

- [SoK: Semantic Decision Engines in Network Control Loops](https://mlsanity.com/paper/2610.06425/) (2026)

## Abstract (arXiv)

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.