论文标题
IEEE 802.11AX网络中的竞争窗口优化，并具有深度加固学习
Contention Window Optimization in IEEE 802.11ax Networks with Deep Reinforcement Learning
论文作者
论文摘要
竞争窗口（CW）值的正确设置对Wi-Fi网络的效率有重大影响。不幸的是，802.11网络使用的标准方法不足以扩展到越来越多的站点维护稳定的吞吐量，但它仍然是802.11ax单用户传输的通道访问的默认方法。因此，我们提出了一种新的CW控制方法，该方法利用深度强化学习（DRL）原则在不同的网络条件下学习正确的设置。我们的方法称为使用DRL（CCOD）的集中竞争窗口优化，支持两种可训练的控制算法：深Q-NETWORK（DQN）和深层确定性策略梯度（DDPG）。我们通过模拟证明，它提供了接近最佳（即使在动态拓扑中）的效率，同时保持计算成本较低。
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.
