Open Access

ARTICLE

Multi-AP Cooperative Radio Resource Allocation Method for Co-Channel Interference Avoidance in 802.11be WLAN

Sujie Shao, Zhengpu Wang^*, Siya Xu, Shaoyong Guo, Xuesong Qiu

School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, 100876, China

* Corresponding Author: Zhengpu Wang. Email:

Computers, Materials & Continua 2025, 84(3), 4949-4972. https://doi.org/10.32604/cmc.2025.065053

Received 02 March 2025; Accepted 03 June 2025; Issue published 30 July 2025

Abstract

With the exponential growth of mobile terminals and the widespread adoption of Internet of Things (IoT) technologies, an increasing number of devices rely on wireless local area networks (WLAN) for data transmission. To address this demand, deploying more access points (APs) has become an inevitable trend. While this approach enhances network coverage and capacity, it also exacerbates co-channel interference (CCI). The multi-AP cooperation introduced in IEEE 802.11be (Wi-Fi 7) represents a paradigm shift from conventional single-AP architectures, offering a novel solution to CCI through joint resource scheduling across APs. However, designing efficient cooperation mechanisms and achieving optimal resource allocation in dense AP environment remain critical research challenges. To mitigate CCI in high-density WLANs, this paper proposes a radio resource allocation method based on 802.11be multi-AP cooperation. First, to reduce the network overhead associated with centralized AP management, we introduce a distributed interference-aware AP clustering method that groups APs into cooperative sets. Second, methods for multi-AP cooperation information exchange, and cooperation transmission processes are designed. To support network state collection, capability advertisement, and cooperative trigger execution at the protocol level, this paper enhances the 802.11 frame structure with dedicated fields for multi-AP cooperation. Finally, considering the mutual influence between power and channel allocation, this paper proposes a joint radio resource allocation algorithm that employs an enhanced genetic algorithm for resource unit (RU) allocation and Q-learning for power control, interconnected via an inner-outer dual-loop architecture. Simulation results demonstrate the effectiveness of the proposed CCI avoidance mechanism and radio resource allocation algorithm in enhancing throughput in dense WLAN scenarios.

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Keywords

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