Title: Unsupervised machine learning schemes for cooperative spectrum sensing in cognitive radio

Authors: Rajendra Yelalwar; Yerram Ravinder

Addresses: Department of Electronics and Telecommunication Engineering, SCTR's Pune Institute of Computer Technology (PICT), Pune, Maharashtra, India ' Department of Electronics and Telecommunication Engineering, SCTR's Pune Institute of Computer Technology (PICT), Pune, Maharashtra, India

Abstract: The major challenge in the development of recent wireless technology is spectrum scarcity which is addressed by introducing the Cognitive Radio (CR) technique. In CR, spectrum sensing is the most critical task that senses the surrounding environment to detect the presence of a Primary User (PU) in the target spectrum. This paper proposes the Machine Learning (ML) enabled Cooperative Spectrum Sensing (CSS) approaches where the application of clustering algorithms for the eigenvalue-based CSS under different fading channel conditions is explored. The sensing performance is analysed with different PUs, signal features, Signal-to-Noise Ratio (SNR) values and channel conditions. Secondly, this work proposes the novel clustering-based CSS framework for Non-Orthogonal Multiple Access (NOMA) signal detection. The simulation results ensure the effectiveness of the proposed clustering-based CSS framework compared to the existing work in terms of improved accuracy which is observed to be 92.5% for K-means clustering-based CSS framework for NOMA.

Keywords: spectrum sensing; machine learning; K-means; K-medoids; agglomerative; NOMA.

DOI: 10.1504/IJCAT.2023.134037

International Journal of Computer Applications in Technology, 2023 Vol.73 No.1, pp.66 - 78

Received: 29 Oct 2021
Accepted: 04 Apr 2022
Published online: 10 Oct 2023 *

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