Next Article in Journal
Markov Chain and Techno-Economic Analysis to Identify the Commercial Potential of New Technology: A Case Study of Motorcycle in Surakarta, Indonesia
Previous Article in Journal
Dynamic Evaluation on the Traffic State of an Urban Gated Community by Opening the Micro-Inter-Road Network
Article Menu
Issue 3 (September) cover image

Export Article

Open AccessArticle
Technologies 2018, 6(3), 72;

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

Department of Electronics and Telecommunication, JSPM’s Rajarshi Shahu College of Engineering, Pune 411033, Maharashtra, India
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)
Full-Text   |   PDF [2846 KB, uploaded 6 August 2018]   |  


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

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Technologies EISSN 2227-7080 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top