A Three-Tier Deep Learning-Based Channel Access Method for WiFi Networks

Future WiFi networks require a channel access method that provides users with high capacity. Such a method must consider 1) channel bonding, which improves the transmission capacity of Access Points (APs); and 2) spatial reuse, where APs tune their Clear Channel Assessment (CCA) threshold and transmit power in order to transmit concurrently with neighboring APs. To date, there are no solutions that
jointly
optimize the channels used by an AP, and the CCA threshold and transmit power of a bonded channel. To this end, we outline a three-tier deep learning approach. Briefly, at Layer-1, it selects a set of transmitting channels. At Layer-2 and Layer-3, it respectively determines the transmit power and CCA threshold for each selected channel. An AP then employs deep reinforcement learning to learn the optimal policy for each layer given its interference intensity and queue length. The simulation results show that when compared to three competing solutions, an AP that uses our approach is able to reduce its queue length by up to 62.52% under realistic traffic load.