Twenty-Five Years of Advances in Beamforming: From convex and nonconvex optimization to learning techniques
Küçük Resim Yok
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee-Inst Electrical Electronics Engineers Inc
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Beamforming 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.
Açıklama
Anahtar Kelimeler
Array signal processing, Shape, Sonar applications, Seismology, Signal processing algorithms, Optimization methods, Machine learning, Channel Estimation, Robust, Signal, Mismatch, Design, Systems, Radar
Kaynak
Ieee Signal Processing Magazine
WoS Q Değeri
Q1
Scopus Q Değeri
Cilt
40
Sayı
4
