Paper Info
Title
Optimizing Distributed MIMO Wi-Fi Networks with Deep Reinforcement Learning.
Abstract
This paper explores the feasibility of leveraging concepts from deep reinforcement learning (DRL) for dynamic resource management in wireless networks. Specifically, this work considers the case of distributed multi-user MIMO (D-MIMO) based Wi-Fi networks and studies how DRL can help improve the performance of these networks, particularly in dynamic scenarios. D-MIMO is a technique by which a set of wireless access points are synchronized and grouped together to jointly serve multiple users simultaneously. This paper addresses two dynamic resource management problems pertaining to D-MIMO Wi-Fi networks in detail -- channel assignment of D-MIMO groups, and deciding how to cluster access points to form D-MIMO groups. A DRL framework is constructed through which an agent interacts with a D-MIMO network, learns about the network environment, and is successful in converging to policies which augment the performance of the D-MIMO network in different scenarios. Through extensive simulations and on-line training based on dense Wi-Fi deployments, this paper demonstrates the merits of using DRL by achieving up to a 20% improvement in the performance of D-MIMO networks with DRL compared to when DRL is not used.
Year
Venue
DocType
2018
arXiv: Information Theory
Journal
Volume
Citations 
PageRank 
abs/1812.06885
0
0.34
References 
Authors
17
6
Name
Order
Citations
PageRank
Neelakantan Nurani Krishnan100.34
Eric Torkildson200.34
Narayan B. Mandayam31471161.08
raychaudhuri41594149.72
Enrico-Henrik Rantala500.34
Klaus Doppler61723153.67