Lower-complexity Wiener filtering for UE-RS channel estimation in LTE DL system

Abstract

Channel estimation algorithms play a key role in 3GPP Long Term Evolution (LTE) Downlink (DL) systems in facilitating coherent detection of data. In this paper, we report the performance of a new scheme for User Equipment-specific Reference Signals (UE-RS) channel estimation, which is 2 1- D Wiener filtering with linear interpolation as a less computationally complex scheme compared to 2-D Wiener filtering and interpolation. Simulations show that 2 1-D Wiener filtering with linear interpolation requires = 34% and = 2.6% of the number of computations needed by 2-D Wiener filtering with linear interpolation and Wiener interpolation, respectively. In addition, it was shown that 2 1-D Wiener filtering performance is sub-optimal when compared with the performance of 2-D Wiener filtering. Moreover, simulations indicate that the best noise reduction method is a combination of both averaging and Wiener filtering with linear interpolation, where the lower bound is a function of both SNR and the channel statistics.