Performance Optimization of Energy-Harvesting Underlay Cognitive Radio Networks Using Reinforcement Learning

TL;DR

This paper employs reinforcement learning, specifically deep Q-networks, to optimize energy harvesting and transmission strategies in underlay cognitive radio networks, improving data rates while managing energy constraints and primary user interference.

Contribution

It introduces a novel DQN-based method for joint energy harvesting and transmission decision-making in energy-constrained cognitive radios, outperforming baseline strategies.

Findings

The proposed method converges effectively.

It achieves higher average data rates.

It outperforms baseline strategies.

Abstract

In this paper, a reinforcement learning technique is employed to maximize the performance of a cognitive radio network (CRN). In the presence of primary users (PUs), it is presumed that two secondary users (SUs) access the licensed band within underlay mode. In addition, the SU transmitter is assumed to be an energy-constrained device that requires harvesting energy in order to transmit signals to their intended destination. Therefore, we propose that there are two main sources of energy; the interference of PUs' transmissions and ambient radio frequency (RF) sources. The SU will select whether to gather energy from PUs or only from ambient sources based on a predetermined threshold. The process of energy harvesting from the PUs' messages is accomplished via the time switching approach. In addition, based on a deep Q-network (DQN) approach, the SU transmitter determines whether to collect energy or transmit messages during each time slot as well as selects the suitable transmission power in order to maximize its average data rate. Our approach outperforms a baseline strategy and converges, as shown by our findings.