Active Precoding

Active precoding optimizes signal transmission in wireless communication systems to mitigate interference and improve data rates, primarily focusing on multi-antenna and multi-user scenarios. Current research heavily utilizes deep learning, particularly reinforcement learning algorithms like DDPG and Soft Actor-Critic, and deep unfolding networks, often integrated with techniques like rate-splitting multiple access (RSMA) and block diagonalization, to design adaptive and robust precoders even with imperfect channel information. This work is significant for enhancing the performance of existing and emerging communication technologies, such as mmWave and terahertz systems, and satellite communications, by improving spectral efficiency and robustness. Furthermore, research explores integrating precoding with other advanced techniques like reconfigurable intelligent surfaces and leveraging data from sensors like LIDAR for improved channel estimation and precoder design.