Paper Info
Title
Multi-Agent Reinforcement Learning for Adaptive User Association in Dynamic mmWave Networks
Abstract
Network densification and millimeter-wave technologies are key enablers to fulfill the capacity and data rate requirements of the fifth generation (5G) of mobile networks. In this context, designing low-complexity policies with local observations, yet able to adapt the user association with respect to the global network state and to the network dynamics is a challenge. In fact, the frameworks proposed in literature require continuous access to global network information and to recompute the association when the radio environment changes. With the complexity associated to such an approach, these solutions are not well suited to dense 5G networks. In this paper, we address this issue by designing a scalable and flexible algorithm for user association based on multi-agent reinforcement learning. In this approach, users act as independent agents that, based on their local observations only, learn to autonomously coordinate their actions in order to optimize the network sum-rate. Since there is no direct information exchange among the agents, we also limit the signaling overhead. Simulation results show that the proposed algorithm is able to adapt to (fast) changes of radio environment, thus providing large sum-rate gain in comparison to state-of-the-art solutions.
Year
DOI
Venue
2020
10.1109/TWC.2020.3003719
IEEE Transactions on Wireless Communications
Keywords
DocType
Volume
Distributed user association,millimeter-wave (mmWave) communications,small cells,deep multi-agent reinforcement learning (MARL)
Journal
19
Issue
ISSN
Citations 
10
1536-1276
4
PageRank 
References 
Authors
0.42
0
5
Name
Order
Citations
PageRank
Sana Mohamed140.42
De Domenico Antonio281.18
Wei Yu332422.95
Yves Lostanlen417720.28
Emilio Calvanese Strinati531135.74