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Recent Advances in Communications Technology
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Recent Advances in Communications Technology

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information and Communications Technology".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 1154

Special Issue Editors

Prof. Dr. Shingchern You

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, Taiwan
Interests: audio signal processing and recognition; applications of digital signal processing to digital communications systems; machine learning
Prof. Dr. Hsiao-Chun Wu

E-Mail Website
Guest Editor
Division of Electrical and Computer Engineering, School of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge, LA, USA
Interests: audio/speech signal processing; machine learning; wireless systems; biometric instrumentation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Technology has brought new opportunities and challenges to communications systems. The Starlink project is a compelling example of how communications technology can affect modern society, especially in terms of warfare. Beyond space-based communication, underwater communication is another area that ought to be researched in greater depth as water absorbs high-frequency electromagnetic waves, making high-speed wireless data transfer difficult. Additionally, machine learning (and, more broadly, AI) technology has penetrated many application domains, meaning that the potential and limitations of machine learning techniques for communications technology are worth exploring. Furthermore, since most communications systems depend on computer simulations for initial performance assessment, simulation-related issues, such as channel characteristics, simulation platform and model, and simulation complexity, are also important. This Special Issue covers, but is not limited to, the above-mentioned topics.

Prof. Dr. Shingchern You
Prof. Dr. Hsiao-Chun Wu
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 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. Information is an international peer-reviewed open access monthly 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 1600 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

  • satellite communications
  • underwater communications
  • machine learning for communications
  • simulations of communications systems

Published Papers (2 papers)

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Research

18 pages, 2433 KiB  
Article
Optimizing the Deployment of an Aerial Base Station and the Phase-Shift of a Ground Reconfigurable Intelligent Surface for Wireless Communication Systems Using Deep Reinforcement Learning
by Wendenda Nathanael Kabore, Rong-Terng Juang, Hsin-Piao Lin, Belayneh Abebe Tesfaw and Getaneh Berie Tarekegn
Information 2024, 15(7), 386; https://doi.org/10.3390/info15070386 - 1 Jul 2024
Viewed by 522
Abstract
In wireless networks, drone base stations (DBSs) offer significant benefits in terms of Quality of Service (QoS) improvement due to their line-of-sight (LoS) transmission capabilities and adaptability. However, LoS links can suffer degradation in complex propagation environments, especially in urban areas with dense [...] Read more.
In wireless networks, drone base stations (DBSs) offer significant benefits in terms of Quality of Service (QoS) improvement due to their line-of-sight (LoS) transmission capabilities and adaptability. However, LoS links can suffer degradation in complex propagation environments, especially in urban areas with dense structures like buildings. As a promising technology to enhance the wireless communication networks, reconfigurable intelligent surfaces (RIS) have emerged in various Internet of Things (IoT) applications by adjusting the amplitude and phase of reflected signals, thereby improving signal strength and network efficiency. This study aims to propose a novel approach to enhance communication coverage and throughput for mobile ground users by intelligently leveraging signal reflection from DBSs using ground-based RIS. We employ Deep Reinforcement Learning (DRL) to optimize both the DBS location and RIS phase-shifts. Numerical results demonstrate significant improvements in system performance, including communication quality and network throughput, validating the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Recent Advances in Communications Technology)
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17 pages, 5348 KiB  
Article
Machine Learning-Based Channel Estimation Techniques for ATSC 3.0
by Yu-Sun Liu, Shingchern D. You and Yu-Chun Lai
Information 2024, 15(6), 350; https://doi.org/10.3390/info15060350 - 13 Jun 2024
Viewed by 430
Abstract
Channel estimation accuracy significantly affects the performance of orthogonal frequency-division multiplexing (OFDM) systems. In the literature, there are quite a few channel estimation methods. However, the performances of these methods deteriorate considerably when the wireless channels suffer from nonlinear distortions and interferences. Machine [...] Read more.
Channel estimation accuracy significantly affects the performance of orthogonal frequency-division multiplexing (OFDM) systems. In the literature, there are quite a few channel estimation methods. However, the performances of these methods deteriorate considerably when the wireless channels suffer from nonlinear distortions and interferences. Machine learning (ML) shows great potential for solving nonparametric problems. This paper proposes ML-based channel estimation methods for systems with comb-type pilot patterns and random pilot symbols, such as ATSC 3.0. We compare their performances with conventional channel estimations in ATSC 3.0 systems for linear and nonlinear channel models. We also evaluate the robustness of the ML-based methods against channel model mismatch and signal-to-noise ratio (SNR) mismatch. The results show that the ML-based channel estimations achieve good mean squared error (MSE) performance for linear and nonlinear channels if the channel statistics used for the training stage match those of the deployment stage. Otherwise, the ML estimation models may overfit the training channel, leading to poor deployment performance. Furthermore, the deep neural network (DNN)-based method does not outperform the linear channel estimation methods in nonlinear channels. Full article
(This article belongs to the Special Issue Recent Advances in Communications Technology)
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