Reinforcement Learning for Efficient Link Scheduling in Multi-Link Networks

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), allowing concurrent transmissions over multiple frequency links. While MLO improves channel access, reduces latency, and boosts reliability, uneven traffic loads may still cause link congestion, starvation, or collisions, which can severely impact TCP flows. This work investigates the impact of MLO on TCP best effort and video traffic flows, and proposes a Reinforcement Learning (RL) Transmission Opportunity (TXOP) Random Discard strategy for adaptive load balancing. Simulation results demonstrate that the proposed AI-driven approach enhances TCP throughput and latency in Wi-Fi 7 multi-link networks.