Artificial Intelligence and Machine Learning Techniques for Microwave Technologies

Dear Colleagues,

Artificial intelligence (AI) and machine learning (ML) technologies have revolutionized various industries, demonstrating their potential to enhance efficiency, accuracy, and innovation. From optimizing microwave circuits to advancing signal processing techniques, AI and ML are indeed paving the way for making unprecedented advancements in the field. The potential applications are vast and varied, encompassing a wide range of topics. In this context, we invite researchers to contribute their original research and review articles to this Special Issue dedicated to "AI and ML Technologies for Microwave Applications".

Topics of interest for this Special Issue include, but are not limited to, the following:

• AI-Optimized Microwave Circuit Design: Utilizing machine learning algorithms to optimize the design and performance of microwave circuits, including filters, amplifiers, and oscillators.
• Machine Learning for Microwave Imaging and Sensing: Exploring the use of AI techniques for improving the resolution, speed, and accuracy of microwave imaging and sensing systems, with applications in remote sensing, medical imaging, and security.
• Deep Learning Techniques for Radar Signal Processing: Investigating the application of deep learning algorithms for radar signal processing tasks such as target detection, classification, and tracking in both civilian and military contexts.
• AI-Driven Beamforming and Antenna Array Optimization: Leveraging artificial intelligence to optimize the design and operation of antenna arrays for beamforming, direction finding, and communication in wireless systems.
• Neural-Network-Based Microwave Communication Systems: Designing and implementing communication systems that utilize neural networks for modulation, demodulation, channel equalization, linearization, and interference mitigation in microwave frequency bands.
• Reinforcement Learning in Microwave System Control: Applying reinforcement learning techniques to optimize the operation and control of microwave systems, including adaptive beamforming, power control, and resource allocation.
• AI-Enhanced Electromagnetic Simulation and Modeling: Enhancing electromagnetic simulation and modeling tools with AI capabilities to enable faster and more accurate analysis of microwave devices, antennas, and propagation phenomena.
• ML Applications in Microwave Medical Diagnostics and Treatments: Investigating the use of machine learning algorithms for improving medical diagnostics and treatments using microwave technologies, such as microwave imaging for breast cancer detection and microwave ablation for tumor therapy.
• Cognitive Radio Systems Empowered by AI Algorithms: Developing cognitive radio systems that utilize AI algorithms for spectrum sensing, dynamic spectrum access, and interference management to optimize spectrum utilization and enhance communication efficiency.
• AI-Driven Optimization of Microwave Power Amplifiers: Employing artificial intelligence techniques to optimize the design and linearization performance of microwave power amplifiers for applications in wireless communication, radar, and satellite systems.

Dr. Pere L. Gilabert
Dr. Telmo Cunha
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.

• artificial intelligence
• machine learning
• microwave technologies
• wireless communications
• radar
• satellite systems