🤖 AI Summary
In energy-constrained cognitive radio networks, secondary users (SUs) must coexist with primary users (PUs) while optimizing performance under stringent energy limitations.
Method: This paper proposes a dynamic joint energy harvesting and data transmission decision framework. It innovatively treats PU communication signals—not as interference but as exploitable radio-frequency (RF) energy—and designs a dual-source adaptive energy harvesting mechanism. Integrating time-switching protocols with a deep Q-network (DQN), the framework jointly optimizes spectrum sensing, transceiver mode switching, and transmit power allocation.
Contribution/Results: The proposed method significantly improves the SU’s average data rate, exhibits stable convergence, and consistently outperforms conventional benchmark strategies across diverse channel conditions and energy constraints. It establishes a novel paradigm for green, self-sustaining cognitive access by enabling SUs to autonomously harvest ambient RF energy from PU transmissions.
📝 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.