Learning to Slice Wi-Fi Networks: A State-Augmented Primal-Dual Approach
- URL: http://arxiv.org/abs/2405.05748v1
- Date: Thu, 9 May 2024 13:13:34 GMT
- Title: Learning to Slice Wi-Fi Networks: A State-Augmented Primal-Dual Approach
- Authors: Yiğit Berkay Uslu, Roya Doostnejad, Alejandro Ribeiro, Navid NaderiAlizadeh,
- Abstract summary: 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.
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.
We show that state augmentation is crucial for generating slicing decisions that meet the ergodic requirements.
- Score: 79.00655335405195
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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