论文标题
波束形成25年的进步：从凸和非凸优化到学习技术
Twenty-Five Years of Advances in Beamforming: From Convex and Nonconvex Optimization to Learning Techniques
论文作者
论文摘要
波束形成是一种信号处理技术，可以使用一系列传感器朝向所需方向进行电磁波转向，形成和聚焦。它已用于多种工程应用中，例如雷达，声纳，声学，天文学，地震学，医学成像和通信。随着多数安德纳纳技术的进步主要用于雷达和通信，对波束形式的设计引起了极大的兴趣，主要依赖于凸/非凸优化。最近，正在利用机器学习来获得更复杂的波束形成问题的有吸引力的解决方案。本文捕获了过去二十五年来从凸到非convex优化和优化对学习方法的射波成型的演变。它可以瞥见这种重要的信号处理技术，使其成为各种发射型架构，传播区，路径和常规/新兴应用。
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.
