  This paper proposes a robust adversarial reinforcement learning (RARL)-based
multi-access point (AP) coordination method that is robust even against
unexpected decentralized operations of uncoordinated APs. Multi-AP coordination
is a promising technique towards IEEE 802.11be, and there are studies that use
RL for multi-AP coordination. Indeed, a simple RL-based multi-AP coordination
method diminishes the collision probability among the APs; therefore, the
method is a promising approach to improve time-resource efficiency. However,
this method is vulnerable to frame transmissions of uncoordinated APs that are
less aware of frame transmissions of other coordinated APs. To help the central
agent experience even such unexpected frame transmissions, in addition to the
central agent, the proposed method also competitively trains an adversarial AP
that disturbs coordinated APs by causing frame collisions intensively. Besides,
we propose to exploit a history of frame losses of a coordinated AP to promote
reasonable competition between the central agent and adversarial AP. The
simulation results indicate that the proposed method can avoid uncoordinated
interference and thereby improve the minimum sum of the throughputs in the
system compared to not considering the uncoordinated AP.
