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Millimeter-Wave Communications for 5G and Beyond
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Millimeter-Wave Communications for 5G and Beyond

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: closed (1 July 2022) | Viewed by 3445

Special Issue Editor

Dr. Shu Sun

E-Mail Website
Guest Editor
Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China
Interests: millimeter-wave;channel modeling

Special Issue Information

Dear Colleague,

With the advent of global commercial deployment of 5G, the important role of millimeter wave (mmWave) (around 30 GHz – 300 GHz) in wireless communications is becoming more evident. MmWave is able to offer unprecedentedly high data rates, high localization and sensing accuracy, and reduced interference due to the abundant bandwidth, tiny wavelength, and the utilization of directional beamforming, respectively, among other benefits. The propagation characteristics and potential beamforming techniques for mmWave have been extensively studied in various scenarios, and the channel models for frequencies up to 100 GHz for terrestrial cellular networks have been standardized in 3GPP specifications.

Nevertheless, the deployment of 5G mmWave systems still face a plurality of challenges, such as the realization of practical mmWave transceiver modules and beamforming architectures, lack of standardized channel models for vehicular-to-everything (V2X) and non-terrestrial mmWave communications, backward compatibility with microwave systems, etc. Furthermore, there are numerous opportunities and research directions for mmWave in beyond-5G (B5G)/6G systems; for instance, the combination with massive antenna arrays or intelligent surfaces, employment of artificial intelligence, and joint communication and sensing in particular.

This Special Issue aims to highlight the deployment challenges and solutions for 5G mmWave systems, and emerging applications of mmWave in B5G/6G. Prospective authors are invited to submit original contributions on, but not limited to, the aforementioned aspects.

Dr. Shu Sun
Guest Editor

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 submissions that pass pre-check are 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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • millimeter wave
  • 5G
  • 6G
  • channel model
  • beamforming
  • sensing
  • joint communication and sensing

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Published Papers (1 paper)

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Research

13 pages, 3116 KiB  
Article
Deep Learning-Based Next-Generation Waveform for Multiuser VLC Systems
by Hafiz M. Asif, Affan Affan, Naser Tarhuni and Kaamran Raahemifar
Sensors 2022, 22(7), 2771; https://doi.org/10.3390/s22072771 - 4 Apr 2022
Cited by 4 | Viewed by 2741
Abstract
Due to the growing number of users, power, and spectral effectiveness, most communication systems are complex and difficult to implement on a large scale. Artificial Intelligence (AI) has played an outstanding role in the implementation of theoretical systems in the real world, with [...] Read more.
Due to the growing number of users, power, and spectral effectiveness, most communication systems are complex and difficult to implement on a large scale. Artificial Intelligence (AI) has played an outstanding role in the implementation of theoretical systems in the real world, with less complexity achieving better results. In this direction, we compare the Non-Orthogonal Multiple Access (NOMA) technique for a multiuser Visible Light Communication (VLC) system with Successive Interference Cancellation (SIC) for two types of detectors: (1) the deep learning-based system and (2) the traditional maximum likelihood (ML) decoder-based system. For multiplexing, we compare the variations of novel Orbital Angular Momentum (OAM) multiplexing and Orthogonal Frequency Division Multiplexing (OFDM) with Index Modulation (IM). In this article, we implement OFDM-IM and OAM-IM for four users for the Gaussian fading MIMO Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) VLC channels. The suggested systems’ bit error rate (BER) performances are compared in simulations for a wide range of Signal-to-Noise Ratios (SNRs), which shows that deep learning-based systems outperform the ML-based system for both users to ensure better decoding at the receiver end, especially at higher SNR values. The detection error is lower in a deep learning-based system at around 20% and around 30% for low SNR and high SNR values, respectively. Full article
(This article belongs to the Special Issue Millimeter-Wave Communications for 5G and Beyond)
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