Contention Window Tuning for Multi-Link Networks Using Reinforcement Learning

Abstract

The increasing demand for higher throughput and lower latency in modern applications has driven the evolution of IEEE 802.11 with the introduction of Wi-Fi 7. The new Extremely High Throughput (EHT) amendment enhances performance with Multi-Link Operation (MLO), which allows Wi-Fi stations equipped with multiple radios to transmit and receive concurrently over different frequency links, improving channel access opportunities, reducing latency, and enhancing overall throughput and reliability. However, in scenarios with irregular or high traffic loads across links, MLO can degrade performance due to contention imbalance and inefficient medium access coordination. In particular, TCP flows are highly sensitive to such conditions, as increased contention and collisions can trigger congestion control mechanisms that reduce sending rates and impair throughput. This work investigates the impact of MLO on TCP traffic and proposes a Reinforcement Learning (RL)-based contention control strategy to improve load distribution in congested multi-link environments. The RL-based method dynamically adapts the channel access aggressiveness of stations according to network conditions, mitigating contention imbalances across links and achieving TCP performance improvements while maintaining or, in some cases, improving UDP performance. Simulation results show that the proposed AI-driven contention control provides a flexible and effective solution for traffic balancing in multi-link Wi-Fi 7 networks.