Low-complexity signal detection for large-scale MIMO systems with second-order Richardson method

Linear minimum mean-square error (MMSE) detection achieves near-optimal performance in large-scale multiple-input multiple-output (LS-MIMO) systems but entails high computational complexity due to large matrix inversion operations. In this Letter, a novel computationally efficient algorithm based on second-order Richardson method is proposed to solve the LS-MIMO detection problem. While no a priori information for the first iteration of the second-order Richardson method is available, the conjugate gradient scheme is exploited that greatly reduces the number of iterations to achieve the desired performance. Moreover, the eigenvalue-based acceleration parameters are proposed to further accelerate the convergence rate. Numerical results demonstrate that the proposed detector outperforms the existing methods and approaches the performance of MMSE with a small number of iterations.