Rate Splitting

Rate-splitting multiple access (RSMA) is a promising interference management technique for next-generation wireless communication systems, aiming to improve spectral efficiency and user throughput by cleverly splitting user data into common and private parts. Current research focuses on optimizing precoding and power allocation strategies within RSMA, often employing deep reinforcement learning (e.g., MADDPG) or meta-learning approaches to adapt to complex channel conditions and imperfect channel state information. These advancements are significant because they enable more efficient resource allocation in multi-user scenarios, particularly in challenging environments like those involving massive MIMO, reconfigurable intelligent surfaces, and energy harvesting, ultimately leading to improved performance in various applications.