A comprehensive multi-agent deep reinforcement learning framework with adaptive interaction strategies for contention window optimization in IEEE 802.11 Wireless LANs

Abstract

This study introduces the Multi-Agent, Multi-Parameter, Interaction-Driven Contention Window Optimization (M2I-CWO) algorithm, a novel Multi-Agent Deep Reinforcement Learning (MADRL) framework designed to optimize multiple CW parameters in IEEE 802.11 Wireless LANs. Unlike single-parameter or specialized multi-agent methods, M2I-CWO employs a Dueling-DQN architecture and an Adaptive Interaction Reward Function—spanning independent, cooperative, competitive, and mixed modes—and accommodates Hierarchical Multi-Agent System (HMAS) or Federated RL (FRL) for further scalability. First, multiple CW parameters are simultaneously adjusted to enhance collision management. Second, M2I-CWO consistently achieves throughput improvements in both static and dynamic scenarios. Extensive results confirm M2I-CWO's superiority in efficiency and adaptability.