Intelligent Power Control for Spectrum Sharing in Cognitive Radios: A Deep Reinforcement Learning Approach

TL;DR

This paper proposes a deep reinforcement learning approach for secondary users in cognitive radio networks to intelligently adjust transmit power, enabling efficient spectrum sharing with primary users without prior knowledge of their strategies.

Contribution

It introduces a novel deep reinforcement learning method for secondary power control in spectrum sharing, addressing unknown primary strategies and environmental sensing.

Findings

Secondary user reaches goal state within few interaction steps

Efficient spectrum sharing achieved without prior knowledge of primary user

Deep RL outperforms traditional power control methods

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. 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 user's transmit power, or 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, which the secondary user can use to intelligently adjust its transmit power such that after a few rounds of interaction with the primary user, both users 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 users can successfully transmit their data) from any initial states within a few number of steps.