Deep Reinforcement Learning-Based Scheduling for Wi-Fi Multi-Access Point Coordination

TL;DR

This paper introduces a deep reinforcement learning approach to optimize scheduling in Wi-Fi networks with multiple access points, significantly reducing worst-case latency and improving network performance in dense deployments.

Contribution

It formulates MAPC scheduling as a sequential decision problem and applies DRL with PPO to enhance Wi-Fi coordination, a novel approach in this context.

Findings

Achieves up to 30% reduction in 99th-percentile delays.

Outperforms heuristic strategies across various network conditions.

Demonstrates robustness in diverse traffic and interference scenarios.

Abstract

Multi-access point coordination (MAPC) is a key feature of IEEE 802.11bn, with a potential impact on future Wi-Fi networks. MAPC enables joint scheduling decisions across multiple access points (APs) to improve throughput, latency, and reliability in dense Wi-Fi deployments. However, implementing efficient scheduling policies under diverse traffic and interference conditions in overlapping basic service sets (OBSSs) remains a complex task. This paper presents a method to minimize the network-wide worst-case latency by formulating MAPC scheduling as a sequential decision-making problem and proposing a deep reinforcement learning (DRL) mechanism to minimize worst-case delays in OBSS deployments. Specifically, we train a DRL agent using proximal policy optimization (PPO) within an 802.11bn-compatible Gymnasium environment. This environment provides observations of queue states, delay metrics, and channel conditions, enabling the agent to schedule multiple AP-station pairs to transmit simultaneously by leveraging spatial reuse (SR) groups. Simulations demonstrate that our proposed solution outperforms state-of-the-art heuristic strategies across a wide range of network loads and traffic patterns. The trained machine learning (ML) models consistently achieve lower 99th-percentile delays, showing up to a 30% improvement over the best baseline.