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IEEE Transactions on Microwave Theory and Techniques Special Issue: AI and Machine Learning Based Technologies for Microwaves

Machine learning and AI have experienced phenomenal success in the past decade in signal processing, image and speech recognition, robotics, autonomous systems and more. This success is also coupled with the expanding applications of machine learning and AI in broad areas of science and engineering. The microwave community is among the earliest in exploring machine learning and artificial neural networks (ANN) for wireless and wireline electronic device, circuit and system designs. In recent years, there is a significant increase in the interests and activities in applying machine learning and AI not only at device/circuit level modeling and design, but also at system and higher-level applications. Stimulated research and applications leads to novel methodologies of microwave oriented machine learning techniques, such as new ANN, support vector machine and Gaussian process based approaches, automated modeling, deep learning; in addition to an expanding scope of microwave problems that are addressed by machine learning and AI, from electromagnetic structural modeling and design, multi-physics modeling, microwave filter/multiplexer design, GaN HEMT modeling, PA behavioral modeling, digital predistortion design, oscillator design, SIW diagnosis, MEM sensor modeling, design of high-speed VLSI packages and microsystems, wireless power transfer, MIMO transmitter design and more. Further applications of machine learning at system level are creating breakthrough capabilities of microwave systems, such as electromagnetic-based image reconstruction for medical or security applications, and dynamic spectrum allocation for next generation wireless systems.