Title: Reinforcement learning-based cooperative sensing in cognitive radio networks for primary user detection

Authors: K. Venkata Vara Prasad; P. Trinatha Rao

Addresses: Department of ECE, Aditya College of Engineering, India ' Department of ECE, GITAM (Deemed to be University), India

Abstract: Cognitive radio networks achieve a better utilisation of spectrum through spectrum sharing. Due to interference, power levels and hidden terminal problem, it becomes challenging to detect the presence of primary users accurately and without this, spectrum sharing cannot be optimised. Thus, detection of primary users has become an important research problem in cognitive radio network. Existing solutions have low accuracy when effect of multipath fading and shadowing are considered. Reinforcement-based learning solutions are able to learn the environment dynamically and able to achieve higher accuracy in detection of primary users. However, the computational complexity and latency is higher in the previous solutions on application of reinforcement learning to spectrum sensing. In this work, reinforcement learning model is proposed to detect the presence of primary user. This approach has higher accuracy due to reliance on multi-objective functions and reduced computational complexity.

Keywords: cooperative spectrum sensing; fusion centre; reinforcement learning; probability of detection; SNR; receiver operating characteristics; ROC.

DOI: 10.1504/IJICS.2022.126752

International Journal of Information and Computer Security, 2022 Vol.19 No.1/2, pp.34 - 47

Received: 02 Dec 2019
Accepted: 17 Apr 2020
Published online: 04 Nov 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article