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Title: A Novel SVM-Based OOK Detector in Low SNR Infrared Channels
Authors: Daba, Jihad S. 
Abdullatif, O.M
Affiliations: Department of Electrical Engineering 
Keywords: Least Square-Support Vector Machine
On-Off Keying
Matched Filter
Maximum Likelihood Detector
Wireless Infrared Communication
Issue Date: 2007
Part of: International journal of electrical robotics, electronics and communications engineering
Volume: 1
Issue: 7
Start page: 1079
End page: 1083
Support Vector Machine (SVM) is a recent class of statistical classification and regression techniques playing an increasing role in applications to detection problems in various engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and communication systems. In this paper, SVM is applied to an infrared (IR) binary communication system with different types of channel models including Ricean multipath fading and partially developed scattering channel with additive white Gaussian noise (AWGN) at the receiver. The structure and performance of SVM in terms of the bit error rate (BER) metric is derived and simulated for these channel stochastic 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 classical binary signal maximum likelihood detection using a matched filter driven by On-Off keying (OOK) modulation. We found that the performance of SVM is superior to that of the traditional optimal detection schemes used in statistical communication, especially for very low signal-to-noise ratio (SNR) ranges. For large SNR, the performance of the SVM is similar to that of the classical detectors. The implication of these results is that SVM can prove very beneficial to IR communication systems that notoriously suffer from low SNR at the cost of increased computational complexity.
Open URL: Link to full text
Type: Journal Article
Appears in Collections:Department of Electrical Engineering

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