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Automatic modulation classification using interacting multiple model - Kalman filter for channel estimation
Abdul Salam, Ahmed O. Sheriff, Ray E. Hu, Yim Fun Al-Araji, S.R. Mezher, K.
Abdul Salam, Ahmed O.
Sheriff, Ray E.
Hu, Yim Fun
Al-Araji, S.R.
Mezher, K.
Publication Date
2019-09
End of Embargo
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Peer-Reviewed
Yes
Open Access status
openAccess
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Abstract
A rigorous model for automatic modulation
classification (AMC) in cognitive radio (CR) systems is proposed
in this paper. This is achieved by exploiting the Kalman filter
(KF) integrated with an adaptive interacting multiple model
(IMM) for resilient estimation of the channel state information
(CSI). A novel approach is proposed, in adding up the squareroot singular values (SRSV) of the decomposed channel using the
singular value decompositions (SVD) algorithm. This new
scheme, termed Frobenius eigenmode transmission (FET), is
chiefly intended to maintain the total power of all individual
effective eigenmodes, as opposed to keeping only the dominant
one. The analysis is applied over multiple-input multiple-output
(MIMO) antennas in combination with a Rayleigh fading channel
using a quasi likelihood ratio test (QLRT) algorithm for AMC.
The expectation-maximization (EM) is employed for recursive
computation of the underlying estimation and classification
algorithms. Novel simulations demonstrate the advantages of the
combined IMM-KF structure when compared to the perfectly
known channel and maximum likelihood estimate (MLE), in
terms of achieving the targeted optimal performance with the
desirable benefit of less computational complexity loads.
Version
Accepted manuscript
Citation
Abdul Salam AO, Sheriff RE, Hu YF et al (2019) Automatic modulation classification using interacting multiple model - Kalman filter for channel estimation. IEEE Transactions on Vehicular Technology. 68(9): 8928-8939.
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Article