  In this paper, a reinforcement-learning-based scheduling framework is
proposed and implemented to optimize the application-layer quality-of-service
(QoS) of a practical wireless local area network (WLAN) suffering from unknown
interference. Particularly, application-layer tasks of file delivery and
delay-sensitive communication, e.g., screen projection, in a WLAN with enhanced
distributed channel access (EDCA) mechanism, are jointly scheduled by adjusting
the contention window sizes and application-layer throughput limitation, such
that their QoS, including the throughput of file delivery and the round trip
time of the delay-sensitive communication, can be optimized. Due to the unknown
interference and vendor-dependent implementation of the network interface card,
the relation between the scheduling policy and the system QoS is unknown.
Hence, a reinforcement learning method is proposed, in which a novel Q-network
is trained to map from the historical scheduling parameters and QoS
observations to the current scheduling action. It is demonstrated on a testbed
that the proposed framework can achieve a significantly better QoS than the
conventional EDCA mechanism.
