Hybrid Beamforming

Hybrid beamforming optimizes wireless communication by combining analog and digital signal processing to create highly directional beams, maximizing data rates and energy efficiency, especially in challenging environments like millimeter-wave systems. Current research focuses on improving the design of these beamformers using machine learning techniques, such as deep learning and Bayesian optimization, to address challenges like imperfect channel knowledge, limited hardware resources (e.g., low-resolution phase shifters), and user selection in multi-user scenarios. These advancements are significant for enabling the deployment of next-generation wireless networks with enhanced performance and reduced environmental impact through improved energy efficiency.