摘要
Abstract
With the increasing of array antennas and the growing complexity of anti-jamming,traditional adaptive beamforming methods often suffer from high computational complexity.Deep learning,with its powerful data-driven capabilities,offers a novel approach to overcoming the performance bottlenecks of traditional adaptive beamforming.This paper provides a systematic review on current studies and development trends of deep learning in array antenna beamforming.First,we revisit the evolution of traditional beamforming algorithms,ranging from the Howells-Applebaum adaptive processor to robust beamforming based on convex optimization.Second,we analyze the innovative applications of deep learning models such as convolutional neural networks(CNNs),recurrent neural networks(RNNs),and long short-term memory(LSTM)networks in beamforming.This review demonstrates that deep learning methods exhibit significant advantages in improving system performance due to their powerful nonlinear modeling capabilities,end-to-end optimization characteristics,and environmental adaptability.Specifically,in mobile communications,deep learning-based beamforming methods substantially enhance the computational efficiency and environmental adaptability of massive multiple input multiple output(MIMO)systems.In radar signal processing,deep learning models effectively improve anti-jamming performance and target detection accuracy.In acoustic signal processing,deep neural networks enable more precise sound source localization and noise suppression.Finally,this paper explores future research directions,including network architecture innovation,real-time processing optimization,robustness enhancement,cross-scenario transfer learning,theoretical foundation deepening,and novel application expansion.关键词
波束形成/深度学习/神经网络/阵列信号处理Key words
beamforming/deep learning/neural networks/array signal processing分类
信息技术与安全科学
