Learning to Slice Wi-Fi Networks: A State-Augmented Primal-Dual Approach
This addresses the limited prior work on slicing in Wi-Fi networks, offering a solution for service differentiation in multi-tenant access points, though it appears incremental as it adapts existing methods to a new context.
The paper tackled the problem of enabling network slicing in Wi-Fi networks to meet quality-of-service requirements, proposing a state-augmented primal-dual learning approach that generates slicing decisions to satisfy ergodic QoS constraints.
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.