Adaptive Beamforming

Adaptive beamforming optimizes signal reception by dynamically adjusting antenna array weights to focus on desired signals while suppressing interference. Current research emphasizes developing computationally efficient algorithms, often leveraging deep learning architectures like convolutional neural networks, transformers, and recurrent neural networks, to improve real-time performance and reduce power consumption in applications such as speech enhancement, wireless communication, and sonar imaging. These advancements are crucial for enabling robust and efficient signal processing in diverse applications, particularly in resource-constrained environments like mobile devices and edge computing systems.