Universal Journal of Electrical and Electronic Engineering 7(6): 320-327, 2020 http://www.hrpub.org DOI: 10.13189/ujeee.2020.070604 Supervised Machine Learning Classifiers for Diversity Combined Signals in 6G Massive MIMO Receivers J. S. Daba 1,* , O. M. Abdul-Latif 2 1 Department of Electrical Engineering, University of Balamand, Lebanon 2 Department of Electrical Engineering, Rochester Institute of Technology, Dubai Received July 24, 2020; Revised September 2, 2020; Accepted September 29, 2020 Cite This Paper in the following Citation Styles (a): [1] J. S. Daba, O. M. Abdul-Latif , "Supervised Machine Learning Classifiers for Diversity Combined Signals in MIMO Receivers," Universal Journal of Electrical and Electronic Engineering, Vol. 7, No. 6, pp. 320 - 327, 2020. DOI: 10.13189/ujeee.2020.070604. (b): J. S. Daba, O. M. Abdul-Latif (2020). Supervised Machine Learning Classifiers for Diversity Combined Signals in MIMO Receivers. Universal Journal of Electrical and Electronic Engineering, 7(6), 320 - 327. DOI: 10.13189/ujeee.2020.070604. Copyright©2020 by authors, all rights reserved. Authors agree that this article remains permanently open access under the terms of the Creative Commons Attribution License 4.0 International License 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. Keywords Least Square-Support Vector Machine, Massive MIMO, M-ary Phase Shift Keying, Orthogonal Frequency Division Multiplexing, Root Mean Square Gain Combining, Single Input Multiple Output, 6G Networks 1. Introduction 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 based on the statistical learning theory initially developed by Vapnik [1] in 1979 and later developed to a more complex concept of structural risk minimization (SRM). 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. In a broad sense, two classes of classifiers are widely used in the literature: (1) model-based classifiers such as the maximum likelihood (ML) and maximum a posterior (MAP) detectors and (2) boundary-based classifiers such as support vector machine, neural networks and fuzzy logic. SVM claims to guarantee generalization, i.e., the decision rules reflect the regularities of the training data rather than the incapability of the learning machine. SVM has been widely used in solving classification and function estimation problems due to its many attractive