J. P. Dubois and Omar M. Abdul-Latif
Least SquareSVM Detector for Wireless BPSK in MultiEnvironmental Noise
1692 - 1697
2008
2
8
International Journal of Electronics and Communication Engineering
https://publications.waset.org/pdf/4231
https://publications.waset.org/vol/20
World Academy of Science, Engineering and Technology
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 modelbased 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 filterdriven detectors,
even in the presence of random Doppler carrier deviation,
especially for low SNR (signaltonoise 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.
Open Science Index 20, 2008