Research on MIMO Linear Precoding Techniques with Algorithm Implementation Analysis

In this research, we conduct an in-depth investigation of MIMO linear precoding techniques, featuring a comprehensive comparative analysis between the Zero-Forcing algorithm and the Block Diagonalization algorithm. MIMO linear precoding represents a sophisticated signal processing methodology for multiple-input multiple-output systems, designed to significantly enhance system performance and communication reliability through intelligent signal preprocessing. The Zero-Forcing algorithm operates as an interference cancellation technique that mathematically nullifies interference by computing the pseudo-inverse of the channel matrix (typically implemented using pinv() function in MATLAB), ensuring minimal inter-user interference at the cost of potential noise enhancement. Meanwhile, the Block Diagonalization algorithm employs advanced matrix decomposition strategies to transform the channel matrix into block-diagonal form through singular value decomposition (SVD) operations, effectively creating parallel non-interfering subchannels for different data streams. This algorithm implementation often involves iterative matrix operations and subspace projections to achieve optimal interference management. Through systematic comparison of these algorithms' implementation complexities, computational requirements, and performance metrics, we gain substantial insights into their respective advantages and limitations, thereby providing valuable guidance for practical applications in modern wireless communication systems.