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Electronics
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Non-orthogonal Multiple-Access Techniques in Next-Generation Wireless Communications
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Non-orthogonal Multiple-Access Techniques in Next-Generation Wireless Communications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 1458

Special Issue Editors

Dr. Xinwei Yue

E-Mail Website
Guest Editor
School of Information and Communication Engineering, Beijing Information Science & Technology University, Beijing 100101, China
Interests: NOMA Networks
Dr. He Sun

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
Interests: non-orthogonal multiple access; intelligent reflecting surface; information theory

Special Issue Information

Dear Colleagues,

With the rapid increase in the requirement for Internet-enabled smart devices and applications, wireless communications are confronted with various unpredictable challenges. As one of the promising types of multiple access, non-orthogonal multiple access (NOMA) has the ability to provide higher spectral efficiency and extensive connections. The aim of this Special Issue is to discuss the information theory and integration of NOMA with new physical layer techniques for future communication systems.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • The information theory in NOMA;
  • Different variants of NOMA;
  • Hybrid multiple access;
  • Grant/semi-frant-free NOMA;
  • Security provisioning in NOMA;
  • Reconfigurable intelligent surface (RIS)-aided NOMA communication;
  • Machine learning-based NOMA communication;
  • Integrated sensing and communication (ISAC)-based NOMA;
  • Enabling NOMA in backscatter communication;
  • NOMA-assisted THz communication;
  • Near-field NOMA communications.

We look forward to receiving your contributions.

Dr. Xinwei Yue
Dr. He Sun
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. Electronics 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 2400 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

  • non-orthogonal multiple access
  • reconfigurable intelligent surface
  • physical layer security
  • integrated sensing and communication
  • 6G communications

Published Papers (1 paper)

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Research

22 pages, 7107 KiB  
Article
A Multi-Modal Modulation Recognition Method with SNR Segmentation Based on Time Domain Signals and Constellation Diagrams
by Ruifeng Duan, Xinze Li, Haiyan Zhang, Guoting Yang, Shurui Li, Peng Cheng and Yonghui Li
Electronics 2023, 12(14), 3175; https://doi.org/10.3390/electronics12143175 - 21 Jul 2023
Cited by 1 | Viewed by 1133
Abstract
Deep-learning-based automatic modulation recognition (AMR) has recently attracted significant interest due to its high recognition accuracy and the lack of a need to manually set classification standards. However, it is extremely challenging to achieve a high recognition accuracy in increasingly complex channel environments [...] Read more.
Deep-learning-based automatic modulation recognition (AMR) has recently attracted significant interest due to its high recognition accuracy and the lack of a need to manually set classification standards. However, it is extremely challenging to achieve a high recognition accuracy in increasingly complex channel environments and balance the complexity. To address this issue, we propose a multi-modal AMR neural network model with SNR segmentation called M-LSCANet, which integrates an SNR segmentation strategy, lightweight residual stacks, skip connections, and an attention mechanism. In the proposed model, we use time domain I/Q data and constellation diagram data only in medium and high signal-to-noise (SNR) regions to jointly extract the signal features. But for the low SNR region, only I/Q signals are used. This is because constellation diagrams are very recognizable in the medium and high SNRs, which is conducive to distinguishing high-order modulation. However, in the low SNR region, excessive similarity and the blurring of constellations caused by heavy noise will seriously interfere with modulation recognition, resulting in performance loss. Remarkably, the proposed method uses lightweight residuals stacks and rich ski connections, so that more initial information is retained to learn the constellation diagram feature information and extract the time domain features from shallow to deep, but with a moderate complexity. Additionally, after feature fusion, we adopt the convolution block attention module (CBAM) to reweigh both the channel and spatial domains, further improving the model’s ability to mine signal characteristics. As a result, the proposed approach significantly improves the overall recognition accuracy. The experimental results on the RadioML 2016.10B public dataset, with SNR ranging from −20 dB to 18 dB, show that the proposed M-LSCANet outperforms existing methods in terms of classification accuracy, achieving 93.4% and 95.8% at 0 dB and 12 dB, respectively, which are improvements of 2.7% and 2.0% compared to TMRN-GLU. Moreover, the proposed model exhibits a moderate parameter number compared to state-of-the-art methods. Full article
(This article belongs to the Special Issue Non-orthogonal Multiple-Access Techniques in Next-Generation Wireless Communications)
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