Awe2021_Article_SecondOrderKalmanFilteringChan (1).pdf (1.31 MB)
Second order Kalman filtering channel estimation and machine learning methods for spectrum sensing in cognitive radio networks
journal contribution
posted on 2021-05-11, 08:06 authored by Olusegun Peter Awe, Daniel Adebowale Babatunde, Sangarapillai LambotharanSangarapillai Lambotharan, Basil AsSadhanWe 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
Find out more...International Scientific Partnership Program (ISPP-18-134(2)) at King Saud University
History
School
- Mechanical, Electrical and Manufacturing Engineering
Published in
Wireless NetworksVolume
27Pages
3273-3286Publisher
SpringerVersion
- VoR (Version of Record)
Rights holder
© The authorsPublisher 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
2021-04-12Publication date
2021-05-10Copyright date
2021ISSN
1022-0038eISSN
1572-8196Publisher version
Language
- en