Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/2190
Title: Least square-SVM detector for wireless BPSK in multi-environmental noise
Authors: Daba, Jihad S. 
Abdul-Latif, Omar M.
Affiliations: Department of Electrical Engineering 
Keywords: Colour noise
Doppler shift
Innovation filter
Least Square-Support Vector Machine
Matched Filter
Rayleigh fading
Wiener filter
Issue Date: 2006
Part of: International journal of electronics and communication engineering
Volume: 12
Issue: 8
Start page: 181
End page: 186
Conference: World Academy of Science, Engineering and Technology (2006 : Cairo, Egypt) 
Abstract: 
Support Vector Machine (SVM) is a statistical learning tool developed to a more complex concept of structural risk minimization (SRM). In this paper, SVM is applied to signal detection in communication systems in the presence of channel noise in various environments in the form of Rayleigh fading, additive white Gaussian background noise (AWGN), and interference noise generalized as additive color Gaussian noise (ACGN). The structure and performance of SVM in terms of the bit error rate (BER) metric is derived and simulated for these advanced stochastic noise models and the computational complexity of the implementation, in terms of average computational time per bit, is also presented. The performance of SVM is then compared to conventional binary signaling optimal model-based detector driven by binary phase shift keying (BPSK) modulation. We show that the SVM performance is superior to that of conventional matched filter-, innovation filter-, and Wiener filter-driven detectors, even in the presence of random Doppler carrier deviation, especially for low SNR (signal-to-noise ratio) ranges. For large SNR, the performance of the SVM was similar to that of the classical detectors. However, the convergence between SVM and maximum likelihood detection occurred at a higher SNR as the noise environment became more hostile.
URI: https://scholarhub.balamand.edu.lb/handle/uob/2190
Open URL: Link to full text
Type: Journal Article
Appears in Collections:Department of Electrical Engineering

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