Machine Learning Applications in Communications and Electronics
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".
Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 1668
Special Issue Editors
Interests: antenna design; microwave components design; wireless communications; evolutionary algorithms; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: analysis and design of digital circuits and systems; power consumption modeling; timing analytical models for digital circuits; leakage detection and localization in metallic pipelines
Special Issues, Collections and Topics in MDPI journals
Interests: IoT; 5G mobile communication; UAV; quality of service; radio access networks; computer network security; radio networks; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: Internet of Things (IoT); edge computing; machine learning; computer vision; cyber physical systems; future Internet architecture and smart-energy
Special Issues, Collections and Topics in MDPI journals
Interests: antenna array design, processing, and characterization; synthesis of complex electromagnetic devices through system-by-design techniques; surrogate-assisted optimization; learning-by-example; deep learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Machine learning (ML), artificial intelligence and its learning, and adaption paradigms are providing an effective solution in engineering applications. ML has the ability to adapt to new conditions and to detect and estimate patterns. Several applications of ML to communications and electronics exist already. These include, among others, Gaussian processes, support vector machines, nearest neighbors, extreme learning machines, evolutionary algorithms (EAs), decision trees, random forests, artificial neural networks (ANNs), and deep learning networks (DNNs). Key enabling technologies for communications and electronics are being increasingly impacted by the utilization of all the aforementioned models. Hybrid combinations of ML algorithms and other approaches are also becoming more common.
This Special Issue aims to publish extended versions of papers in machine learning communications and electronics. Potential topics include but are not limited to the following:
- Machine learning techniques for wireless communications;
- Machine learning techniques for propagation modeling;
- Machine learning techniques for antenna design;
- Machine learning techniques for other EM problems;
- Machine learning techniques for 5G networks and beyond;
- Machine learning techniques for VLSI design;
- Machine Learning techniques for signal processing;
- Machine Learning techniques for music processing;
- Machine Learning techniques for leakage detection problems;
- Machine Learning techniques for wired and wireless networks;
- ML techniques for biomedical applications and wireless monitoring;
- Surrogate models for antenna design problems;
- Other innovative ML techniques;
- Explainable ML for communications and electronics.
Dr. Sotirios K. Goudos
Prof. Dr. Spyridon Nikolaidis
Dr. Panagiotis Sarigiannidis
Dr. Shaohua Wan
Dr. Marco Salucci
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
- deep neural networks
- random forests
- support vector machines
- extreme learning machines
- Gaussian processes
- artificial neural networks (ANNs)
- ensemble learning methods
- image analysis
- audio analysis
