ReinWiFi: Application-Layer QoS Optimization of WiFi Networks with Reinforcement Learning

TL;DR

This paper introduces ReinWiFi, a reinforcement learning framework that optimizes application-layer QoS in WiFi networks by dynamically adjusting scheduling parameters, outperforming traditional EDCA mechanisms in real-world tests.

Contribution

The paper presents a novel RL-based scheduling framework for WiFi QoS optimization that adapts to unknown interference and vendor-specific hardware, improving performance over existing methods.

Findings

ReinWiFi significantly improves throughput and delay metrics.

The RL approach adapts effectively to interference and hardware variations.

Performance surpasses traditional EDCA in testbed experiments.

Abstract

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.