Problem
Research questions and friction points this paper is trying to address.
heterogeneous Wi-Fi networks
multi-link operation (MLO)
throughput optimization
legacy STAs
coexistence
EvoOMG: An Evolution-Oriented Multi-Agent Guidance Framework for Heterogeneous Legacy-and-MLO Wi-Fi Networks
🤖 AI Summary
This work addresses the throughput optimization challenge arising from the long-term coexistence of single-link and multi-link operation (MLO) devices in Wi-Fi networks by proposing an evolution-oriented multi-agent guidance framework. The approach formulates scheduling as a phased decision problem aligned with the “contention-then-transmission” MAC timing, generating contention and aggregation directives autoregressively while explicitly differentiating protocol behaviors between the two device types. Leveraging multi-agent reinforcement learning, the method integrates channel conditions, queue states, and historical context into its state representation and embeds standard-specific feasibility constraints to condition action generation. NS-3 simulations demonstrate that the proposed framework significantly outperforms static EDCA, single-step MADDPG, and independent learning baselines in terms of effective throughput, convergence stability, and MLO link utilization.
📝 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 single-link 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 standard-constrained 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multi-Agent Reinforcement Learning
Wi-Fi MLO
Heterogeneous Networks
MAC Protocol Optimization
Staged Decision Making
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