ReinWiFi: Application-Layer QoS Optimization of WiFi Networks with Reinforcement Learning
This work addresses QoS optimization in WiFi networks for applications like file transfer and real-time communication, representing an incremental improvement over existing methods.
The authors tackled the problem of WiFi networks' inability to adapt to specific QoS needs, network conditions, and interference by proposing a reinforcement learning framework that jointly schedules file delivery and delay-sensitive tasks, achieving significantly better performance than the standard EDCA mechanism.
The enhanced distributed channel access (EDCA) mechanism is used in current wireless fidelity (WiFi) networks to support priority requirements of heterogeneous applications. However, the EDCA mechanism can not adapt to particular quality-of-service (QoS) objective, network topology, and interference level. In this paper, a novel reinforcement-learning-based scheduling framework is proposed and implemented to optimize the application-layer quality-of-service (QoS) of a WiFi network with commercial adapters and unknown interference. Particularly, application-layer tasks of file delivery and delay-sensitive communication are jointly scheduled by adjusting the contention window sizes and application-layer throughput limitation, such that the throughput of the former and the round trip time of the latter can be optimized. Due to the unknown interference and vendor-dependent implementation of the WiFi adapters, 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 performance than the EDCA mechanism.