Paper Info
Title
Intelligent Power Control for Spectrum Sharing: A Deep Reinforcement Learning Approach.
Abstract
We consider the problem of spectrum sharing in a cognitive radio system consisting of a primary user and a secondary user. The primary user and the secondary user work in a non-cooperative manner, and independently adjust their respective transmit power. Specifically, the primary user is assumed to update its transmit power based on a pre-defined power control policy. The secondary user does not have any knowledge about the primary useru0027s transmit power, neither its power control strategy. The objective of this paper is to develop a learning-based power control method for the secondary user in order to share the common spectrum with the primary user. To assist the secondary user, a set of sensor nodes are spatially deployed to collect the received signal strength information at different locations in the wireless environment. We develop a deep reinforcement learning-based method for the secondary user, based on which the secondary user can intelligently adjust its transmit power such that after a few rounds of interaction with the primary user, both the primary user and the secondary user can transmit their own data successfully with required qualities of service. Our experimental results show that the secondary user can interact with the primary user efficiently to reach a goal state (defined as a state in which both the primary and the secondary users can successfully transmit their own data) from any initial states within a few number of iterations.
Year
Venue
Field
2017
arXiv: Information Theory
Mathematical optimization,Transmitter power output,Wireless,Power control,Computer network,Signal strength,Spectrum sharing,Mathematics,Cognitive radio,Reinforcement learning
DocType
Volume
Citations 
Journal
abs/1712.07365
3
PageRank 
References 
Authors
0.39
24
5
Name
Order
Citations
PageRank
Jun Fang162.14
Xingjian Li231.41
Wen Cheng342.89
Z. Chen443238.14
Hongbin Li513711.40