Special Issue "Machine Learning for 5G Communications and Beyond"

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Information and Communication Technologies".

Deadline for manuscript submissions: 1 November 2018

Special Issue Editors

Guest Editor
Dr. David Martín-Sacristán

iTEAM Research Institute, Universitat Politècnica de València, Valencia 46022, Spain
Website | E-Mail
Interests: 5G; vehicular communications; mobile and wireless communications; simulation and modelling; radio resource management; machine learning
Guest Editor
Dr. Sandra Roger

iTEAM Research Institute, Universitat Politècnica de València, Valencia 46022, Spain
Website | E-Mail
Interests: 5G; vehicular communications; mobile and wireless communications; massive MIMO; machine learning

Special Issue Information

Dear Colleagues,

Machine learning has recently gained importance due to the increased availability of data and computing power. Its application to mobile and wireless communication technologies is in an infant state; however, it is generally agreed that machine learning will be a key element to enable new applications and to increase efficiency. In fact, for 5G and beyond, machine learning is considered of paramount importance to provide low latency and high reliability. In addition, network operators expect great benefits from machine learning with regard to the design, control and optimization of networks. To get the most of machine learning, wireless networks will undergo many changes in its architecture, protocol stacks, data formats, etc. We invite researchers to contribute original papers describing the application and impact of machine learning in 5G communications and beyond. Potential topics include, but are not limited to, the following:

Machine learning in radio interface protocols

Machine learning for network operation and management

Machine learning in software defined networking (SDN) and network function virtualization (NFV)

Machine learning for spectrum management

Machine learning for resource allocation

Machine learning for Massive MIMO

Machine learning for green communications

Machine learning for prediction of subscriber behavior

Machine learning and self-organizing networks (SON)

Machine learning for Internet of things (IoT)

Machine learning for vehicular communications

Dr. David Martín-Sacristán
Dr. Sandra Roger
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Technologies is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 350 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • 5G
  • Machine learning
  • Mobile and wireless communications

Published Papers (1 paper)

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Research

Open AccessArticle Channel Estimation and Data Detection Using Machine Learning for MIMO 5G Communication Systems in Fading Channel
Technologies 2018, 6(3), 72; https://doi.org/10.3390/technologies6030072
Received: 23 May 2018 / Revised: 13 July 2018 / Accepted: 26 July 2018 / Published: 6 August 2018
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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
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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. Full article
(This article belongs to the Special Issue Machine Learning for 5G Communications and Beyond)
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