The proper setting of contention window (CW) values has a significant impact on the efficiency of Wi-Fi networks. Unfortunately, the standard method used by 802.11 networks is not scalable enough to maintain stable throughput for an increasing number of stations, yet it remains the default method of channel access for 802.11ax single-user transmissions. Therefore, we propose a new method of CW control, which leverages deep reinforcement learning (DRL) principles to learn the correct settings under different network conditions. Our method, called centralized contention window optimization with DRL (CCOD), supports two trainable control algorithms: deep Q-network (DQN) and deep deterministic policy gradient (DDPG). We demonstrate through simulations that it offers efficiency close to optimal (even in dynamic topologies) while keeping computational cost low.
翻译:不幸的是,802.11个网络使用的标准方法不足以维持越来越多的台站的稳定输送量,然而,它仍然是802.11x单一用户传输的默认通道接入方法,因此,我们提出了一种新的CW控制方法,利用深度强化学习(DRL)原则学习不同网络条件下的正确设置。我们的方法,即与DRL(CCOD)的集中辩论窗口优化,支持两种可训练的控制算法:深Q网络(DQN)和深层确定性政策梯度(DDPG),我们通过模拟来证明它提供了接近最佳的效率(即使在动态表层中),同时保持计算成本低。