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Deep Reinforcement Learning for Multi-Agent Power Control in Heterogeneous Networks

Authors :
Lin Zhang
Ying-Chang Liang
Source :
IEEE Transactions on Wireless Communications. 20:2551-2564
Publication Year :
2021
Publisher :
Institute of Electrical and Electronics Engineers (IEEE), 2021.

Abstract

We consider a typical heterogeneous network (HetNet), in which multiple access points (APs) are deployed to serve users by reusing the same spectrum band. Since different APs and users may cause severe interference to each other, advanced power control techniques are needed to manage the interference and enhance the sum-rate of the whole network. Conventional power control techniques first collect instantaneous global channel state information (CSI) and then calculate sub-optimal solutions. Nevertheless, it is challenging to collect instantaneous global CSI in the HetNet, in which global CSI typically changes fast. In this article, we exploit deep reinforcement learning (DRL) to design a multi-agent power control algorithm, which has a centralized-training-distributed-execution framework. To be specific, each AP acts as an agent with a local deep neural network (DNN) and we propose a multiple-actor-shared-critic (MASC) method to train the local DNNs separately in an online trial-and-error manner. With the proposed algorithm, each AP can independently use the local DNN to control the transmit power with only local observations. Simulations results show that the proposed algorithm outperforms the conventional power control algorithms in terms of both the converged average sum-rate and the computational complexity.

Details

ISSN :
15582248 and 15361276
Volume :
20
Database :
OpenAIRE
Journal :
IEEE Transactions on Wireless Communications
Accession number :
edsair.doi...........d1cc07a79d11aeb82c90fa8d6f6fbc9d