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, as a supervised machine learning tool, is playing an increasing role in applications to detection problems in various engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and 6G wireless communication networks. In this paper, SVM is applied to signal detection in 6G 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 signaling 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. Finally, the SVM-based detector is implemented in an uplink SIMO system using both Equal Gain Combiner (EGC) technique and Root Mean Square Gain Combiner (RMSGC) technique in which the later technique will be proven to be superior to the earlier.
Highlights
Support Vector Machine (SVM) is a recent class of statistical classification and regression techniques getting an increased attention on its application to classification problems in various engineering areas
SVM is formulated on the structural risk minimization (SRM) principle which minimizes an upper bound on the generalization error, as opposed to the classical empirical risk minimization (ERM) approach which minimizes the error on the training data and is embodied in statistical learning
The results shown in figure 8 prove once again the superiority of the SVM-based detector over the classical maximum likelihood (ML) detector, and show the performance of the SIMO system that doesn’t adopt the Orthogonal Frequency Division Multiplexing (OFDM) technology and emphasizes on the effect of the additive noise on the multiple antennae
Summary
Support Vector Machine (SVM) is a recent class of statistical classification and regression techniques getting an increased attention on its application to classification problems in various engineering areas. SVM is formulated on the structural risk minimization (SRM) principle which minimizes an upper bound on the generalization error, as opposed to the classical empirical risk minimization (ERM) approach which minimizes the error on the training data and is embodied in statistical learning. To the best of our knowledge, SVM has not been implemented yet for receiver detection in digital communication systems in the presence of advanced additive colour receiver noise and multiplicative channel fading noise. Notable exceptions are the initial work of Dubois and Abdel-Latif [7] who applied SVM to OOK-infrared channels in a local fading environment with partially developed multipath fading and additive white Gaussian receiver noise (AWGN), and the work of Mokbel and Hashem [8] who applied SVM to a BNRZ detector (sampler and comparator) using multiple samples per binary period in the presence of AWGN in wire-line communication systems
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