Learning to Slice Wi-Fi Networks: A State-Augmented Primal-Dual Approach

TL;DR

This paper introduces a novel unsupervised learning framework using a state-augmented primal-dual algorithm to enable flexible network slicing in Wi-Fi networks, ensuring QoS requirements are met.

Contribution

It proposes a new learning-based approach with state augmentation for Wi-Fi slicing, addressing the lack of prior solutions in this area.

Findings

State augmentation improves QoS compliance.

The method effectively optimizes slicing decisions.

Dual variables are updated online for adaptability.

Abstract

Network slicing is a key feature in 5G/NG cellular networks that creates customized slices for different service types with various quality-of-service (QoS) requirements, which can achieve service differentiation and guarantee service-level agreement (SLA) for each service type. In Wi-Fi networks, there is limited prior work on slicing, and a potential solution is based on a multi-tenant architecture on a single access point (AP) that dedicates different channels to different slices. In this paper, we define a flexible, constrained learning framework to enable slicing in Wi-Fi networks subject to QoS requirements. We specifically propose an unsupervised learning-based network slicing method that leverages a state-augmented primal-dual algorithm, where a neural network policy is trained offline to optimize a Lagrangian function and the dual variable dynamics are updated online in the execution phase. We show that state augmentation is crucial for generating slicing decisions that meet the ergodic QoS requirements.