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

2024-05-09

arXiv.org

AI Summary

To address the challenge of differentiating Quality-of-Service (QoS) and Service-Level Agreement (SLA) guarantees for heterogeneous traffic in Wi-Fi networks, this paper proposes a constraint-aware, unsupervised learning–driven network slicing framework. Operating under a single-access-point, multi-tenant architecture, it introduces— for the first time in Wi-Fi slicing—a state-augmented primal-dual optimization: a neural policy is trained offline to optimize the Lagrangian function, while dual variables are updated online in real time; the state-augmentation mechanism ensures strict satisfaction of ergodic QoS constraints. The method integrates unsupervised learning, Lagrangian relaxation, and channel-level dynamic scheduling. Evaluated on realistic traffic models, it achieves over a 37% improvement in SLA compliance rate compared to baseline methods, accelerates convergence by 2.1×, and significantly enhances the guaranteeability of critical QoS metrics—including latency and throughput.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Network Slicing
• Wi-Fi Networks
• Quality of Service

Innovation

• Unsupervised Learning
• Network Slicing
• Adaptive Resource Allocation