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Title: Supervised Machine Learning Classifiers for Diversity Combined Signals in 6G Massive MIMO Receivers
Authors: Daba, Jihad S. 
Abdul-Latif, Omar M.
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
Issue Date: 2020
Part of: Universal Journal of Electrical and Electronic Engineering
Volume: 7
Issue: 6
Start page: 320
End page: 327
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.
DOI: 10.13189/ujeee.2020.070604
Open URL: Link to full text
Type: Journal Article
Appears in Collections:Department of Electrical Engineering

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