Cognitive Radio Spectrum Sensing and Prediction Using Deep Reinforcement Learning

Syed Qaisar Jalil, Stephan Chalup, Mubashir Husain Rehmani

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

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 efficiently. The task of CSS is performed as follows. Each secondary user (SU) performs local sensing and using CQL algorithm, determines the presence of licensed user for current and k-1 future timeslots. These results are forwarded to the fusion centre where another CQL algorithm is operating that generates a global decision for the current and k-1 future timeslots. Then, SUs do not perform sensing for the next k-1 timeslots to save energy. The proposed CSS mechanism can significantly increase the licensed user detection accuracy and the data transmissions by SUs. In addition, it reduces the sensing results transmission overhead. The proposed solution is tested with a stochastic traffic load model for different activity patterns. Our simulation results show that the proposed problem formulation using the CQL algorithm can achieve similar detection accuracy as other state-of-the-art methods for CSS while significantly reducing the computation time.

Original languageEnglish
Title of host publicationIJCNN 2021 - International Joint Conference on Neural Networks, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780738133669
DOIs
Publication statusPublished - 18 Jul 2021
Externally publishedYes
Event2021 International Joint Conference on Neural Networks, IJCNN 2021 - Virtual, Shenzhen, China
Duration: 18 Jul 202122 Jul 2021

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2021-July

Conference

Conference2021 International Joint Conference on Neural Networks, IJCNN 2021
Country/TerritoryChina
CityVirtual, Shenzhen
Period18/07/202122/07/2021

Keywords

  • cognitive radio
  • Cooperative spectrum sensing
  • deep reinforcement learning
  • spectrum occupancy prediction

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