A Reinforcement Learning-Based Approach to Optimize Network Performance in Wireless LANs

Abstract: The IEEE 802.11 wireless networks commonly employ the distributed coordination function (DCF) for distributed channel access among competing stations. However, the conventional contention-based backoff mechanism suffers from increased collisions, packet loss, latency, and reduced throughput under varying network loads and asymmetric traffic conditions. In this project, we present a lightweight reinforcement learning (RL)-based adaptive contention-window mechanism specifically designed to enhance the IEEE 802.11 DCF protocol. Utilizing tabular Q- learning, the proposed method maintains a Q-table to learn optimal contention-window adjustment strategies based on real-time network states. The RL agent dynamically decides to increase, decrease, or maintain the contention window, guided by a reward function that considers successful transmission rate, end-to-end latency, MAC failures, and queue pressure. Implementation and evaluation were conducted using the NS-3 network simulator across diverse traffic scenarios with varying traffic rates and transmission start times, simulating different contention levels and asymmetric traffic patterns. Comparative performance analysis against the standard IEEE 802.11 DCF and Enhanced Distributed Channel Access (EDCA) protocols demonstrates that the RL-based approach effectively improves wireless network performance under dynamic contention conditions. This project investigates a lightweight and interpretable reinforcement learning framework for adaptive medium access control in IEEE 802.11 networks.

Keywords: IEEE 802.11, DCF, EDCA, Reinforcement Learning, Tabular Q-Learning, Adaptive Contention Window, Wireless Networks