Abstract:Block diagonalization (BD) belongs to a traditional linear precoding algorithm with multiple inputs and outputs, and its core idea is to find the orthogonal basis of the null space in interference matrixes through singular value decomposition (SVD), so as to eliminate the multiuser interference (MUI). However, as the number of transmitters and receivers increases, the BD precoding algorithm faces more complex computation, which has become one of the key factors restricting its development. Therefore, this study proposes an optimal low-complexity BD algorithm. The algorithm is based on the combination algorithm of Schmidt orthogonalization inversion and lattice reduction operation in orthogonal decomposition, and it replaces the SVD of two high-complexity operations on the traditional BD algorithm by Schmidt orthogonalization inversion and lattice reduction operation and thus reduces the algorithm complexity. The results show that the computational complexity of the optimal algorithm is reduced by 46.7%, and the system and capacity are increased by 2–10 bits/Hz. Furthermore, the bit error rate is improved by two orders of magnitude.