RIS-Assisted Low Complexity Precoding for Decentralized Massive MU-MIMO System

Massive multi-user multiple-input multiple-output (MU-MIMO) antenna arrays are pivotal for the advancement of next-generation communication system. However, under non-line-of-sight (NLoS) conditions, the performance improvements provided by Massive MU-MIMO are limited, and the complexity associated with precoding design approaches becomes overwhelming, particularly in scenarios involving multiuser variations. The advent of reconfigurable intelligent surfaces (RIS) offers a promising solution, enabling substantial performance enhancements under NLoS conditions while maintaining a low cost. Nevertheless, the complexity of precoding design schemes in RIS-assisted system with large-scale antenna arrays remains a significant challenge. In response to this issue, this paper introduces the star-decentralized minimum mean square error Tomlinson-Harashima precoding (sDMMSE-THP) algorithm and integrates it into the star decentralized baseband processing (DBP) framework. This combination not only reduces the computational complexity but also delivers superior system performance. To further mitigate the issue of increasing precoding update complexity due to user variations, we propose an update algorithm for sDMMSE-THP that avoids redundant computations arising from the number of user changes, thus ensuring that the overall system complexity remains manageable. Simulation results demonstrate that the proposed DBP approach for precoding in RIS-assisted system, particularly in large-scale antenna array scenarios, effectively maintains high performance while significantly reducing system complexity. These findings underscore the importance and potential of the proposed methodology in enhancing the efficiency of future communication system.