Second order Kalman filtering channel estimation and machine learning methods for spectrum sensing in cognitive radio networks

• Published in: Wireless networks Vol. 27; no. 5; pp. 3273 - 3286
• Main Authors: Awe, Olusegun Peter, Babatunde, Daniel Adebowale, Lambotharan, Sangarapillai, AsSadhan, Basil
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2021; Springer Nature B.V
• Subjects: Algorithms; Centroids; Cluster analysis; Clustering; Cochannel interference; Cognitive radio; Communications Engineering; Computer Communication Networks; Electrical Engineering; Engineering; Estimation; IT in Business; Kalman filters; Machine learning; Networks; Performance degradation; Polynomials; Radio networks; Vector quantization; Wireless networks; Mobile secondary users; Spectrum sensing; Cognitive radio; Channel estimation; Kalman filter; Machine learning
• ISSN: 1022-0038; 1572-8196
• DOI: 10.1007/s11276-021-02627-w

We address the problem of spectrum sensing in decentralized cognitive radio networks using a parametric machine learning method. In particular, to mitigate sensing performance degradation due to the mobility of the secondary users (SUs) in the presence of scatterers, we propose and investigate a classifier that uses a pilot based second order Kalman filter tracker for estimating the slowly varying channel gain between the primary user (PU) transmitter and the mobile SUs. Using the energy measurements at SU terminals as feature vectors, the algorithm is initialized by a K -means clustering algorithm with two centroids corresponding to the active and inactive status of PU transmitter. Under mobility, the centroid corresponding to the active PU status is adapted according to the estimates of the channels given by the Kalman filter and an adaptive K -means clustering technique is used to make classification decisions on the PU activity. Furthermore, to address the possibility that the SU receiver might experience location dependent co-channel interference, we have proposed a quadratic polynomial regression algorithm for estimating the noise plus interference power in the presence of mobility which can be used for adapting the centroid corresponding to inactive PU status. Simulation results demonstrate the efficacy of the proposed algorithm.