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|Title:||Improved M-ary Signal Detection Using Support Vector Machine Classifiers||Authors:||Daba, Jihad S.
Abdul-Latif, Omar M.
|Affiliations:||Department of Electrical Engineering||Keywords:||Least Square-Support Vector Machine
M-ary Phase Shift Keying
Fully Developed Rayleigh Fading
|Issue Date:||2005||Part of:||Proceedings of World Academy of Science, Engineering and Technology||Start page:||264||End page:||268||Conference:||International Enformatika Conference (IEC) (26-28 August 2005 : Prague, Czech Republic)||Abstract:||
Support Vector Machine (SVM) is a statistical learning tool that was initially developed by Vapnik in 1979 and later developed to a more complex concept of structural risk minimization (SRM). SVM is 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 signal detection in communication systems in the presence of channel noise in the form of fully developed Rayleigh multipath fading and receiver 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 M-ary signalling optimal model-based detector driven by M-ary phase shift keying (MPSK) modulation. We show that the SVM performance is superior to that of conventional detectors which require as much as 7 bits-coding (M ≥ 128) to produce comparable results to those of SVM.
|URI:||https://scholarhub.balamand.edu.lb/handle/uob/622||Open URL:||Link to full text||Type:||Conference Paper|
|Appears in Collections:||Department of Electrical Engineering|
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