Awe2021_Article_SecondOrderKalmanFilteringChan.pdf (1.32 MB)

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

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journal contribution
posted on 11.05.2021, 08:06 by Olusegun Peter Awe, Daniel Adebowale Babatunde, Sangarapillai Lambotharan, Basil AsSadhan
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.

Funding

Communications Signal Processing Based Solutions for Massive Machine-to-Machine Networks (M3NETs)

Engineering and Physical Sciences Research Council

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International Scientific Partnership Program (ISPP-18-134(2)) at King Saud University

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Wireless Networks

Publisher

Springer

Version

VoR (Version of Record)

Rights holder

© The authors

Publisher statement

This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

Acceptance date

12/04/2021

Publication date

2021-05-10

ISSN

1022-0038

eISSN

1572-8196

Language

en

Depositor

Prof Sangarapillai Lambotharan. Deposit date: 10 April 2021

Licence

Exports