Scalable Interference Graph Learning for Low-Latency Wi-Fi Networks using Hashing-based Evolution Strategy

TL;DR

This paper introduces a scalable interference graph learning framework using hashing-based evolution strategy to optimize RTWT scheduling in Wi-Fi 7, significantly improving efficiency and reducing latency in dense networks.

Contribution

It proposes a novel scalable interference graph learning method with a deep hashing function and evolution strategy for efficient RTWT scheduling in dense Wi-Fi networks.

Findings

Improves slot efficiency by up to 25%.

Reduces packet losses by up to 30%.

Reduces training and inference time by 4 and 8 times, respectively.

Abstract

Wi-Fi 7 introduces the restricted target wake time (RTWT) mechanism, which is vital for Industrial IoT (IIoT) applications requiring periodic, reliable, and low-latency communication. RTWT enables deterministic channel access by assigning scheduled transmission slots to stations (STAs), minimizing contention and interference. However, determining efficient RTWT slot assignments remains challenging in dense networks, where conventional interference graph-based models lack flexibility and scalability. To overcome this, we propose a scalable interference graph learning (IGL) framework that learns optimal interference graph representations for graph coloring-based RTWT scheduling. The IGL leverages an evolution strategy (ES) to train a neural network (NN) using a single network-wide reward, avoiding costly edge-wise feedback. Furthermore, a deep hashing function (DHF) groups interfering STAs, limiting training and inference to relevant subsets and greatly reducing complexity. Simulation results demonstrate that the proposed IGL improves slot efficiency by up to 25\%, reduces packet losses by up to 30\% in dynamic environments. Thanks to DHF, it also reduces the training and inference time of IGL by 4 and 8 times, respectively, and the online slot assignment time by 3 times in large networks.