Low-Complexity Segment Training Channel Estimation in Cloud Radio Access Networks

Cloud radio access networks (C-RANs) have attracted considerable attention because of the capability of meeting the exponential increasing traffic demand in the future communication systems. In this paper, we consider the segment training based channel estimation in C-RANs. As the classical minimum mean-square-error estimator has cubic complexity in the dimension of the covariance matrices, due to the inversion operation, we propose a low-complexity channel estimator by means of the \emph{L}-degree matrix polynomial expansion, which can significantly reduce the computational complexity without degrading much performance. The numerical results are presented to verify the proposed channel estimators, and the simulation results show there are significant performance gains from our proposal.