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Electronics
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Advancements in Communication and Sensing Systems through Machine Learning
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Advancements in Communication and Sensing Systems through Machine Learning

A special issue of Electronics (ISSN 2079-9292).

Deadline for manuscript submissions: closed (15 October 2024) | Viewed by 1448

Special Issue Editors

Dr. Huan Wu

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: optical signal processing; machine learning; artificial intelligence; optical sensors
Special Issues, Collections and Topics in MDPI journals
Dr. Hua Zheng

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: digital signal processing; fiber-optic system; distributed optical fiber sensor
Dr. Kun Zhu

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: lidar; optical signal processing; microwave photonics; nonlinear optics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the transformative role of machine learning in advancing communication and sensing systems. This Special Issue will include the latest research and advancements in machine learning techniques and their applications in enhancing the efficiency, reliability, and performance of communication and sensing systems.

The scope of this Special Issue includes, but is not limited to, machine learning algorithms for signal processing, optimization of communication systems, sensor data analysis, and the development of intelligent of sensing technologies. It will also cover the integration of machine learning with internet of things (IoT) for improved data communication and sensing capabilities.

This Special Issue invites original research articles, review articles, and case studies that provide insights into the use of machine learning in communication and sensing systems. It aims to serve as a platform for researchers, academicians, and industry professionals to share their innovative research and findings.

We look forward to receiving your contributions.

Dr. Huan Wu
Dr. Hua Zheng
Dr. Kun Zhu
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

  • machine learning
  • communication systems
  • sensing systems
  • signal processing
  • sensor data analysis
  • intelligent sensing technologies
  • internet of things (IoT)

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Published Papers (1 paper)

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Research

15 pages, 722 KiB  
Article
Deep Learning-Based Multi-Feature Fusion for Communication and Radar Signal Sensing
by Ting Li, Tian Liu, Zhangli Song, Lin Zhang and Yiming Ma
Electronics 2024, 13(10), 1872; https://doi.org/10.3390/electronics13101872 - 10 May 2024
Cited by 1 | Viewed by 1181
Abstract
Recent years witness the rapid development of communication and radar technologies, and many transmitters are equipped with both communication and radar functionalities. To keep track of the working state of a target dual-functional transmitter, it is crucial to sense the modulation mode of [...] Read more.
Recent years witness the rapid development of communication and radar technologies, and many transmitters are equipped with both communication and radar functionalities. To keep track of the working state of a target dual-functional transmitter, it is crucial to sense the modulation mode of the emitted signals. In this paper, we propose a deep learning-based intelligent modulation sensing technique for dual-functional transmitters. Different from existing modulation sensing methods, which usually focus on communication signals, we take both communication and radar signals into consideration. Typically, the dominant features of communication signals lie in the time domain, while those of radar signals lie in both time and frequency domains. To enhance the sensing accuracy, we first exploit real and complex value convolution operations to extract both time-domain and frequency-domain features of emitted signals from the target transmitter. Then, we fuse the extracted features by assigning proper weights with the attention mechanism. Simulation results reveal that the proposed technique can improve the sensing accuracy by up to 4% on average compared with benchmarks. Full article
(This article belongs to the Special Issue Advancements in Communication and Sensing Systems through Machine Learning)
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