TY - JFULL AU - J. P. Dubois and Omar M. Abdul-Latif PY - 2008/9/ TI - Least Square-SVM Detector for Wireless BPSK in Multi-Environmental Noise T2 - International Journal of Electronics and Communication Engineering SP - 1691 EP - 1697 VL - 2 SN - 1307-6892 UR - https://publications.waset.org/pdf/4231 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 20, 2008 N2 - 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. ER -