From Design to Deployment of Zero-touch Deep Reinforcement Learning WLANs

2022/07/13 by Ovidiu Iacoboaiea, Iacoboaiea, Ovidiu, Jonatan Krolikowski +5
Computer Science · Engineering · #Advanced Wireless Network Optimization #Energy Harvesting in Wireless Networks #FOS: Computer and information sciences #Networking and Internet Architecture (cs.NI) #Wireless Networks and Protocols

paper · pdf · doi:10.48550/arxiv.2207.06172

openalex publication_date 2022/07/13 · openalex created_date 2022/07/15 · openalex updated_date 2026/07/28

Abstract

Machine learning (ML) is increasingly used to automate networking tasks, in a paradigm known as zero-touch network and service management (ZSM). In particular, Deep Reinforcement Learning (DRL) techniques have recently gathered much attention for their ability to learn taking complex decisions in different fields. In the ZSM context, DRL is an appealing candidate for tasks such as dynamic resource allocation, that is generally formulated as hard optimization problems. At the same time, successful training and deployment of DRL agents in real-world scenarios faces a number of challenges that we outline and address in this paper. Tackling the case of Wireless Local Area Network (WLAN) radio resource management, we report guidelines that extend to other usecases and more general contexts.

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