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Technologies 2018, 6(3), 72; https://doi.org/10.3390/technologies6030072

Channel Estimation and Data Detection Using Machine Learning for MIMO 5G Communication Systems in Fading Channel

1
Department of Electronics and Telecommunication, JSPM’s Rajarshi Shahu College of Engineering, Pune 411033, Maharashtra, India
2
Department of Electronics and Telecommunication, Dr. D. Y. Patil Institute of Technology, Pimpri, Pune 411018, Maharashtra, India
*
Author to whom correspondence should be addressed.
Received: 23 May 2018 / Revised: 13 July 2018 / Accepted: 26 July 2018 / Published: 6 August 2018
(This article belongs to the Special Issue Machine Learning for 5G Communications and Beyond)
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Abstract

In multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems, multi-user detection (MUD) algorithms play an important role in reducing the effect of multi-access interference (MAI). A combination of the estimation of channel and multi-user detection is proposed for eliminating various interferences and reduce the bit error rate (BER). First, a novel sparse based k-nearest neighbor classifier is proposed to estimate the unknown activity factor at a high data rate. The active users are continuously detected and their data are decoded at the base station (BS) receiver. The activity detection considers both the pilot and data symbols. Second, an optimal pilot allocation method is suggested to select the minimum mutual coherence in the measurement matrix for optimal pilot placement. The suggested algorithm for designing pilot patterns significantly improves the results in terms of mean square error (MSE), symbol error rate (SER) and bit error rate for channel detection. An optimal pilot placement reduces the computational complexity and maximizes the accuracy of the system. The performance of the channel estimation (CE) and MUD for the proposed scheme was good as it provided significant results, which were validated through simulations. View Full-Text
Keywords: channel estimation; evolutionary algorithm; machine learning; multiuser detection channel estimation; evolutionary algorithm; machine learning; multiuser detection
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Motade, S.N.; Kulkarni, A.V. Channel Estimation and Data Detection Using Machine Learning for MIMO 5G Communication Systems in Fading Channel. Technologies 2018, 6, 72.

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