Hierarchical Reinforcement Learning for Next Generation of Multi-AP Coordinated Spatial Reuse

TL;DR

This paper introduces a hierarchical reinforcement learning framework using a two-layer Multi-Armed Bandit algorithm to optimize coordinated spatial reuse in multi-AP Wi-Fi networks, improving throughput and fairness.

Contribution

It proposes a novel two-layer MAB-based approach for multi-AP coordination, reducing overhead and complexity compared to classical methods.

Findings

Enhanced sum-throughput in simulations.

Improved fairness among network nodes.

Robust performance across various network scenarios.

Abstract

In next generation of Wi-Fi networks Multiple Access Point Coordination (MAPC) is poised to significantly enhance the network performance by enabling a set of Access Points (APs) to coordinate with each other through advanced coordinating schemes so that to reduce inter-AP contention and congestion. This paper focuses on defining a framework to facilitate the coordination across multi-APs when these employ Coordinated Spatial Reuse (C-SR). In this case, the coordinating APs may need to reciprocally adjust their scheduling strategy, power control and link adaptation to meet specific Quality of Service (QoS) requirements, which by using classical approaches leads to high overhead due to negotiations needed across APs, and requires complex solutions in order to properly optimize the network across all the parameters in play. In this matter, a two layer Multi-Armed Bandit (MAB) algorithm has been proposed to optimize such a network while preserving the fair use of resources across all nodes. The validity of this holistic approach is confirmed by system level simulations, which show that the proposed algorithm not only improves the network in terms of sum-throughput, but also allows to enhance fairness, making this a robust solution for next-generation of Wi-Fi networks.