  Carrier sensing multiple access/collision avoidance (CSMA/CA) is the backbone
MAC protocol for IEEE 802.11 networks. However, tuning the binary exponential
back-off (BEB) mechanism of CSMA/CA in user-dense scenarios so as to maximize
aggregate throughput still remains a practically essential and challenging
problem. In this paper, we propose a new and enhanced multiple access mechanism
based on the application of deep reinforcement learning (DRL) and Federated
learning (FL). A new Monte Carlo (MC) reward updating method for DRL training
is proposed and the access history of each station is used to derive a
DRL-based MAC protocol that improves the network throughput vis-a-vis the
traditional distributed coordination function (DCF). Further, federated
learning (FL) is applied to achieve fairness among users. The simulation
results showcase that the proposed federated reinforcement multiple access
(FRMA) performs better than basic DCF by 20% and DCF with
request-to-send/clear-to-send (RTS/CTS) by 5% while guaranteeing the fairness
in user-dense scenarios.
