EvoOMG Evolution-Oriented Multi-Agent Guidance Framework for Heterogeneous Legacy and MLO Wi-Fi Networks

Jul 16, 2026

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Wi‑Fi 7/8 multi‑link operation (MLO) rollout creates long‑term coexistence between legacy non‑MLO stations and MLO‑capable stations, making throughput optimization difficult under mixed behaviors. Existing learning methods treat the network as homogeneous and map observations directly to full MAC actions, failing to capture legacy single‑link contention and transmission versus MLO multi‑link access. EvoOMG reformulates throughput optimization as a standard‑constrained staged multi‑agent decision problem with contention guidance followed by aggregation guidance.

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Introduction

Motivation and challenge in mixed-standard WLANs

Behavioral decomposition via evolution-oriented staged control

EvoOMG: An Evolution-Oriented Multi-Agent Guidance Framework for Heterogeneous Legacy-and-MLO Wi-Fi Networks Junjie Wu, Lingjian Zhou, Zerui Shao, Yi Zou, Tianrui Li Senior Member, IEEE, Yi Zhang Senior Member, IEEE, Ziyuan Yang Member, IEEE arXiv :2607 .07045v 1 [ cs .NI] 8 Jul 2026 Abstract—The gradual deployment of Wi-Fi 7/8 multi-link operation (MLO) will lead to long-term coexistence between legacy non-MLO stations (STAs) and MLO-capable STAs in WLANs. This mixed deployment makes throughput optimization challenging because legacy STAs follow single-link contention and transmission, whereas MLO-capable STAs can exploit multiple links with richer access opportunities. Existing learning-based methods usually treat such networks as homogeneous systems and directly map the current observation to a complete MAC action, which cannot faithfully represent both legacy singlelink and MLO multi-link behaviors. To address this issue, we propose EvoOMG, an evolution-oriented multi-agent guidance framework for heterogeneous legacy-and-MLO Wi-Fi networks. EvoOMG reformulates throughput optimization as a standardconstrained staged multi-agent decision problem. Each agent encodes recent channel, queue, contention, and transmission histories, first generates contention guidance, and then produces aggregation guidance conditioned on the preceding access stage and standard-specific feasibility constraints. This autoregressive design follows the Wi-Fi MAC order of “contention before transmission” while preserving distinct protocol behaviors of legacy and MLO-capable STAs. NS-3 evaluations show that EvoOMG improves scheduled goodput, convergence stability, and MLO link utilization over static enhanced distributed channel access (EDCA), one-step MADDPG, and independent-learning baselines, achieving substantial performance gains in representative mixed-standard scenarios. Index Terms—Wi-Fi 7/8, legacy-and-MLO coexistence, multilink operation, multi-agent guidance, autoregressive control, heterogeneous WLAN optimization. I. INTRODUCTION WIRELESS local area networks (WLANs) are entering a long coexistence period in which non-multi-link operation stations (STAs) and emerging multi-link operation (MLO)-capable Wi-Fi 7/8 stations operate in the same deployment. This coexistence makes Wi-Fi throughput optimization J. Wu, Z. Shao, and T, Li are with the School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China. (e-mail: [jjw@swjtu.edu.cn](jjw@swjtu.edu.cn), [zeruishao.zr@gmail.com](zeruishao.zr@gmail.com), [trli@swjtu.edu.cn](trli@swjtu.edu.cn)). L. Zhou and Y. Zou are with the School of Software, Nanchang Hangkong University, Nanchang 330063, China. (e-mail: [24201534@stu.nchu.edu.cn](24201534@stu.nchu.edu.cn), [71410@nchu.edu.cn](71410@nchu.edu.cn)) . Y. Zhang is with the School of Cyber Science and Engineering, Sichuan University, Chengdu 610207, China. (e-mail: [yzhang@scu.edu.cn](yzhang@scu.edu.cn)). Z. Yang is with the Nanyang Technological University, Singapore 639798, Singapore. (e-mail: [cziyuanyang@gmail.com](cziyuanyang@gmail.com)). This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. substantially more challenging than in conventional homogeneous WLANs [1], [2] . Since different device types exhibit distinct transmission behaviors and channel-access opportunities, the optimization must account for both device heterogeneity and their asymmetric access to wireless resources [3],[4] . In homogeneous WLAN scenarios, STAs usually follow similar single-link access mechanisms and expose homogeneous medium access control (MAC) configuration spaces. Under this setting, existing methods can effectively optimize MAC-layer contention and scheduling policies, since the underlying optimization reduces to a relatively stationary resource allocation problem and is not affecte

FAQs

Why is throughput optimization challenging in mixed legacy and MLO Wi‑Fi deployments?

Legacy non‑MLO STAs contend and transmit over a single link, while MLO‑capable STAs can exploit multiple links and follow different feasibility rules. This creates asymmetric, standard-dependent feasible actions and access behaviors.

What limitation do existing learning-based approaches have?

They typically treat the network as homogeneous and map observations directly to a complete MAC action, which cannot faithfully represent both legacy single-link behavior and MLO multi-link behavior.

How does EvoOMG structure the decision-making process?

EvoOMG reformulates optimization as a standard‑constrained staged multi‑agent problem: agents generate contention guidance first, then produce aggregation guidance conditioned on the preceding access stage and standard-specific feasibility constraints.

Jul 16, 2026

Upload by Skyler