Paper Info
Title
Power Allocation for Full-Duplex Communication Systems Based on Deep Deterministic Policy Gradient
Abstract
In full-duplex (FD) communications, the base station (BS) can communicate with an uplink user and a downlink user simultaneously on the same frequency band. Therefore, there exist two kinds of interference in FD communications, the self-interference and the co-channel interference. On the other hand, power allocation policy plays an important role in dealing with the interference. In this paper, a deep reinforcement learning method is applied to optimize the power allocation, aiming at maximizing the spectrum efficiency for FD systems. The optimization problem is a non-convex one. Then, a deep deterministic policy gradient (DDPG)-based power allocation algorithm is proposed to allocate downlink transmit power. Accordingly, the uplink transmit power can be adjusted adaptively. With the proposed DDPG-based algorithm, the BS can intelligently adjust its downlink transmit power by interacting with the environment. Numerical results show that the proposed algorithm can achieve a near optimal performance with much lower computational complexity than traditional approaches.
Year
DOI
Venue
2020
10.1109/GCWkshps50303.2020.9367558
2020 IEEE Globecom Workshops (GC Wkshps
Keywords
DocType
ISSN
full-duplex communication systems,base station,BS,uplink user,downlink user,FD communications,co-channel interference,power allocation policy,deep reinforcement learning method,FD systems,deep deterministic policy gradient-based power allocation algorithm,downlink transmit power,uplink transmit power,DDPG-based algorithm
Conference
2166-0069
ISBN
Citations 
PageRank 
978-1-7281-7308-5
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Jin Qu100.68
Congliang Zhu200.34
Shengli Liu348445.70
Guanding Yu41287101.15
Rui Yin512911.38