Contention Window Optimization.
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, in  the authors propose a new method of CW control, which leverages deep reinforcement learning (DRL) principles to learn the correct settings under different network conditions. Their method, called centralized contention window optimization with DRL (CCOD), supports two trainable control algorithms: deep Qnetwork (DQN) and deep deterministic policy gradient (DDPG). They demonstrate through simulations that it offers efficiency close to optimal (even in dynamic topologies) while keeping computational cost low.
The authors published their simulation code in the name “RLinWiFi” at GitHub repository. This article tries to repeat their experiments on ns3-gym which was installed in a chroot jail environment. This article explains the same procedure presented in ; but, this procedure is done under chroot jail based installation.
Since we already installed ns3-gym as described in our previous article, we decided to use the same for installing RLinWiFi.
Configuring and compiling ns3-gym again
$ ./waf configure