Joint Position and Beamforming Control via Alternating Nonlinear Least-Squares with a Hierarchical Gamma Prior

Parayil, Anjaly; Bedi, Amrit Singh; Koppel, Alec

doi:10.23919/acc50511.2021.9482851

Cited by 2 publications

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…p 2 = The solution for ( 10) is achieved via iterative reweighted MVDR techniques [40]. In addition to generalizing the noise model, a specific choice of priors over the distribution of the beamforming weights may also be used in, say, sparsity-driven beamforming [41].…”

Section: Beamforming With Worst Case Performance Optimizationmentioning

confidence: 99%

Twenty-Five Years of Advances in Beamforming: From convex and nonconvex optimization to learning techniques

Elbir

Mishra

Vorobyov

et al. 2023

IEEE Signal Process. Mag.

View full text Add to dashboard Cite

eamforming is a signal processing technique to steer, shape, and focus an electromagnetic (EM) wave using an array of sensors toward a desired direction. It has been used in many engineering applications, such as radar, sonar, acoustics, astronomy, seismology, medical imaging, and communications. With the advent of multiantenna technologies in, say, radar and communication, there has been a great interest in designing beamformers by exploiting convex or nonconvex optimization methods. Recently, machine learning (ML) is also leveraged for obtaining attractive solutions to more complex beamforming scenarios. This article captures the evolution of beamforming in the last 25 years from convex to nonconvex optimization and optimization to learning approaches. It provides a glimpse into these important signal processing algorithms for a variety of transmit-receive architectures, propagation zones, propagation paths, and multidisciplinary applications.

show abstract

Section: Beamforming With Worst Case Performance Optimizationmentioning

confidence: 99%