AI-Powered Antenna and Radio Frequency Technologies

Dear Colleagues,

Modern wireless systems (for 5G/6G, satellite Internet access, radio astronomy, autonomous driving, biomedical imaging, etc.) require complex phased arrays with close integration of antennas and beamforming electronics. Until recently, radio frequency (RF) designers used solely conventional numerical analysis methods (i.e., method of moments, finite element method, finite difference time domain) through electromagnetic (EM) simulators. Artificial intelligence (AI) techniques (such as support vector machines, extreme learning machines and artificial neural networks) are now being proposed to challenge and/or assist these conventional approaches.

AI tools have the ability to learn any non-linear input–output relationship at any design/test phase and provide accurate physical responses in the development and characterization of the RF components and systems. Designers can take advantage of AI algorithms by generating trained EM models for fast optimization of design parameters on different goal functions in order to perform fast stochastic analyses or to quickly assess various what-if scenarios. Current and future wireless systems require new ideas, techniques, and applications to understand and exploit the full potential of AI in antenna and RF technologies.

In this Special Issue, original research articles and reviews on AI techniques for electromagnetics are welcome. Research areas in communication, radar, and sensing applications may include (but are not limited to) the following:

• AI in antenna element/array optimization;
• AI techniques in adaptive beamforming (interference cancellation, direction of arrival estimation, etc.);
• AI in RF integrated circuit (IC) development;
• AI for design and simulation of microwave components and EM surfaces/lenses;
• AI in circuit routing and layout design;
• AI for modeling and predicting mutual coupling;
• AI for assessing antenna and beamforming robustness;
• AI for antenna diagnostics;
• AI for array calibration;
• AI for multiple beam generating networks;
• AI for modeling scattering;
• AI in object detection and classification;
• AI for body area networks.

Authors from both academia and industry are encouraged to submit papers on the proposed topics in the form of theoretical and empirical studies.

We look forward to receiving your contributions.

Dr. Yanki Aslan
Dr. Mobayode O. Akinsolu
Dr. Maria Kovaleva
Guest Editors

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