The IEEE 802.11 MAC layer utilizes the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) mechanism for channel contention, but dense Wi-Fi deployments often cause high collision rates. To address this, this paper proposes an intelligent channel contention access mechanism that combines Federated Learning (FL) and Deep Deterministic Policy Gradient (DDPG) algorithms. We introduce a training pruning strategy and a weight aggregation algorithm to enhance model efficiency and reduce MAC delay. Using the NS3-AI framework, simulations show our method reduces average MAC delay by 25.24\% in static scenarios and outperforms A-FRL and DRL by 25.72\% and 45.9\% in dynamic environments, respectively.

翻译：IEEE 802.11 MAC层采用带冲突避免的载波侦听多址接入（CSMA/CA）机制进行信道竞争，但密集的Wi-Fi部署常导致高冲突率。为解决此问题，本文提出一种结合联邦学习（FL）与深度确定性策略梯度（DDPG）算法的智能信道竞争接入机制。我们引入一种训练剪枝策略和权重聚合算法，以提升模型效率并降低MAC时延。通过NS3-AI框架进行仿真，结果表明：在静态场景中，我们的方法将平均MAC时延降低了25.24%；在动态环境中，其性能分别优于A-FRL和DRL方法25.72%和45.9%。