Twenty-Five Years of Advances in Beamforming: From Convex and Nonconvex Optimization to Learning Techniques

Ahmet M. Elbir; Kumar Vijay Mishra; Sergiy A. Vorobyov; Robert W. Heath

doi:10.1109/MSP.2023.3262366

paper

Twenty-Five Years of Advances in Beamforming: From Convex and Nonconvex Optimization to Learning Techniques

arXiv:2211.02165 · doi:10.1109/MSP.2023.3262366

Abstract

Beamforming is a signal processing technique to steer, shape, and focus an electromagnetic wave using an array of sensors toward a desired direction. It has been used in several engineering applications such as radar, sonar, acoustics, astronomy, seismology, medical imaging, and communications. With the advances in multi-antenna technologies largely for radar and communications, there has been a great interest on beamformer design mostly relying on convex/nonconvex optimization. Recently, machine learning is being leveraged for obtaining attractive solutions to more complex beamforming problems. This article captures the evolution of beamforming in the last twenty-five years from convex-to-nonconvex optimization and optimization-to-learning approaches. It provides a glimpse of this important signal processing technique into a variety of transmit-receive architectures, propagation zones, paths, and conventional/emerging applications.

Accepted Paper in IEEE Signal Processing Magazine Special Issue on the 75th Anniversary of IEEE Signal Processing Society

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An Overview of Advances in Signal Processing Techniques for Classical and Quantum Wideband Synthetic Apertures

Twenty-Five Years of Advances in Beamforming: From Convex and Nonconvex Optimization to Learning Techniques

Abstract

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