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Least Square-SVM Detector for Wireless BPSK in Multi-Environmental Noise
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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1331763Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1534
 V. Vapnik, "Estimation of Dependences Based on Empirical Data," Nauka English Translation, Springer Verlag, 1982.
 J. Weston and C. Watkins, "Support Vector Machines from Multi-Class Pattern Recognition," University Of London, unpublished.
 S. Chen, S. Gunn, and C. Harris, "Decision Feedback Equalizer Design Using Support Vector Machines," Inst. Elect. Eng. Proc. Vision, Image and Signal Processing, vol. 147, no. 3, 2000, pp. 213-219.
 D. Sebald and J. Bucklew, "Support Vector Machine Techniques for Non Linear Equalization," IEEE Trans. Sig. Proc., vol. 48, 2000, pp. 3217-66.
 F. Albu and D. Martinez, "The Application of SVM with Gaussian Kernels for Overcoming CCI," IEEE Int. Workshop Neural Net., 1999, pp. 49-57.
 F. Perez-Cruz, A. Vazquez, P. Dianna, and A. Rodriguez, "SVM-Based Equalizer for Burst TDMA Transmission," Signal Processing, 2000.
 J. P. Dubois and O. Abdul-Latif, "A Novel SVM-Based OOK Detector in Low SNR Infrared Channels," International Conference on Signal Processing, Prague, Czech Republic, Aug. 2005, submitted.
 J. P. Dubois and O. Adbellatif, "Improved M-ary Signal Detection Using Support Vector Machine Classifiers," International Conference on Signal Processing, Czech Republic, Aug. 2005.
 J. P. Dubois and O. Abdellatif, "SVM-Based Detection of SAR Images in Partially Developed Speckle Noise," WEC2005, 5th World Enformatika Conference, Czech Republic, Aug. 2005.
 J. P. Dubois and O. Abdellatif, "Detection of Ultrasonic Images in the Presence of a Random Number of Scatterers: A Statistical Learning Approach," WEC2005, 5th World Enformatika Conference, Czech Republic, Aug. 2005
 J. C. Mokbel and F. Hashem, "Support Vector Machines in Digital Communication," Master Thesis, Univ. of Balamand, Lebanon, 2003.
 IEEE Std 802.11-1999: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications ÔÇö the overarching IEEE 802.11 specification. Available htttp://standards.ieee.org/getieee802/download/802.11-1999.pdf
 M. Simon and M. Alouini, Communication over Fading Channels. New Jersey: Wiley-IEEE Press, 2005.
 L. Gagliardi and S. Karp, Optical Communications. New York: Wiley, 1995.
 J. P. Dubois, "Scattering Statistics of Doppler Faded Acoustic Signals Using Speckle Noise Models", VIIIth International Conference on Direct and Inverse Problems of Electromagnetic and Acoustic Wave Theory, IEEE MTT/ED/AP/CPMT/SSC, Lviv, September 2003.
 N. Christianini and J. Taylor, "Support Vector Machine and Other Kernel Learning Methods". London: Cambridge University Press, 2003.
 J. Christopher and C. Burges, "A Tutorial on Support Vector Machines for Pattern Recognition", Kulwer Publishers, vol. 12, no. 2, 1998, pp. 121-167.
 T. Joachims, "Support Vectors and Kernel Methods", Cornell University.
 J. Suykers and J. Vardewalle, "Multi-Class Least Square-Support Vector Machine," Universite Catholique de Louvain, Belgium, unpublished.
 K. Lin and C. Lin, "A Study on Reduced Support Vector Machines," IEEE Transaction on Neural Networks, vol. 14, no. 6, 2003.
 D. Anguita, A. Boni, and S. Ridella, "A Digital Architecture for SVM," IEEE Trans. Neural Networks, vol. 14, no. 5, 2003, pp. 993-1000.
 M. Tipping, "The Relevance Vector Machine," in Advances in Neural Information Processing Systems. Cambridge: MIT Press, 2000.