Low complexity signal detector based on Lanczos method for large-scale MIMO systems

For large-scale multiple-input multiple-output (MIMO) systems, linear minimum mean square error (MMSE) method is one of the most near-optimal ways for signal detection. However, MMSE involves matrix inversion which is of high complexity for computation. In this paper, a Lanczos-based method is proposed to solve the problem by transferring the matrix inversion computation into an iteration process of solving a linear equation. The simulation result proves that the BER of Lanczos-based method outperforms the recently proposed Neumann series approximation algorithm with the same SNR. At the same time, the Lanczos-based method could reduce the computational complexity from O(K
³
) to O(K
²
), where K is the number of users. Then the convergence speed is analysed by Kaniel-Paige-Saad theory. Finally, the result shows that the performance of Lanczos-based method achieves the classical MMSE exact matrix inversion methods with only a small number of iterations.