Low-Complexity Channel Estimation Based On Weighted Kapteyn Series Expansion For Massive Mimo Systems

An improved MMSE method aimed to reduce the computational burden is proposed in this paper. In the method, the covariance matrix inversion is approximated by the expansion of N-order polynomials of weighted Kapteyn series which has low truncation errors. In this method, an unconstrained non-linear optimization model is established and an iterative algorithm based on coordinate rotation is used to solve this problem. Analysis and numerical results show that the mean square error (MSE) of our proposed weighted Kapteyn-MMSE estimation approaches MMSE performance quickly when the series order of N is increasing, while the computational complexity of this method is significantly reduced. And the proposed method improves the convergence to ordinary MMSE performance when compared to traditional Taylor-MMSE estimator or Kapteyn-MMSE, which means the latter needs a higher N-order than the former to approach MMSE estimation.