  The existing medium access control (MAC) protocol of Wi-Fi networks (i.e.,
carrier-sense multiple access with collision avoidance (CSMA/CA)) suffers from
poor performance in dense deployments due to the increasing number of
collisions and long average backoff time in such scenarios. To tackle this
issue, we propose an intelligent wireless MAC protocol based on deep learning
(DL), referred to as DL-MAC, which significantly improves the spectrum
efficiency of Wi-Fi networks. The goal of DL-MAC is to enable not only
intelligent channel access but also intelligent rate adaptation. To achieve
this goal, we design a deep neural network (DNN) that takes the historical
received signal strength indications (RSSIs) as inputs and outputs joint
channel access and rate adaptation decision. Notably, the proposed DL-MAC takes
the constraints of practical applications into account and the DL-MAC is
evaluated using the real wireless data sampled from the actual environments on
the 2.4GHz frequency band. The experimental results show that our DL-MAC can
achieve around 86\% performance of the global optimal MAC, and around the
double performance of the traditional Wi-Fi MAC in the environments of our lab
and the Shenzhen Baoan International Airport departure hall.
