Cognitive Radio Spectrum Sensing and Prediction Using Deep Reinforcement Learning

• Published in: Proceedings of ... International Joint Conference on Neural Networks pp. 1 - 8
• Main Authors: Jalil, Syed Qaisar, Chalup, Stephan, Rehmani, Mubashir Husain
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18.07.2021
• Subjects: cognitive radio; Computational modeling; Cooperative spectrum sensing; deep reinforcement learning; Neural networks; Prediction algorithms; Reinforcement learning; Simulation; spectrum occupancy prediction; Stochastic processes; Telecommunication traffic
• ISSN: 2161-4407
• DOI: 10.1109/IJCNN52387.2021.9533497

In this paper, we propose to use deep reinforcement learning (DRL) for the task of cooperative spectrum sensing (CSS) in a cognitive radio network. We selected a recently proposed offline DRL method called conservative Q-learning (CQL) due to its ability to learn complex data distributions efficient...