Precoding Matrix

Precoding matrices are signal processing techniques used to optimize data transmission in wireless communication systems, primarily aiming to maximize data rate, minimize power consumption, and mitigate interference. Current research heavily focuses on leveraging machine learning, particularly deep neural networks (including graph neural networks and autoencoders), and reinforcement learning algorithms to design adaptive and robust precoding solutions, often addressing challenges like non-linear power amplifier distortion and imperfect channel state information. These advancements are significant because they promise improved spectral efficiency and energy efficiency in various applications, such as massive MIMO, vehicular networks, and satellite communications.