LS-SVM detector for RMSGC diversity in SIMO channels

Abstract

Support vector machine (SVM) is a statistical learning tool developed to a more complex concept of structural risk minimization. SVM is playing an increasing role in applications to detection problems in statistical signal processing and communication systems. In this paper, SVM is applied to the detection of root-mean-square-gain combining (RMSGC) diversity signals in single-input-multiple-output (SIMO) systems, in the presence of channel noise characterized as partially developed Rician multipath fading 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 analysed for these advanced stochastic noise models. The performance of SVM is then compared to conventional SIMO signaling with optimal model-based detection. We show that the SVM performance is superior to that of the maximum likelihood detector for all the selected pre-detection diversity gain combining schemes.