LMMSE channel estimation for OFDM systems with channel correlation function selection

Abstract

In the linear minimum mean square error (LMMSE) estimation for orthogonal frequency division multiplexing (OFDM) systems, the channel correlation function (CCF) is required. Some methods have been proposed to calculate the CCF. Instead of providing a novel method to obtain the CCF, a scheme is developed for the estimator to select among different CCFs. In this paper, an enhanced LMMSE estimation is proposed that is able to select the best-matched CCF within a candidate set. To this end, a parameter comparison scheme is proposed, in which the possible channel statistics for the LMMSE estimation can be evaluated using the sampled noise MSE. Analytical expressions are thus derived to indicate the accuracy of the proposed scheme. Furthermore, fuzzy bound is provided as the performance metric, which reflects the resolution of the parameter comparison scheme. As an example of application, the enhanced LMMSE method is used with the block pilot pattern in the OFDM systems, and the possible CCF candidates for typical scenarios are presented. The complexity of the estimator is also analyzed and a simplified parameter comparison algorithm is proposed to reduce the complexity. Finally, the theoretical analysis and performance comparison are demonstrated by simulation experiments.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Iterative Joint Estimation Procedure of Channel and PDP for OFDM Systems

“…However, the approximation in these methods results in performance deterioration. Additionally, Mei [ 18 ] proposes to design a candidate set of CCF vectors in advance and then selects the optimal one to construct the channel correlation matrix. This scheme can still achieve good MSE performance when the channel knowledge is completely unknown.…”

“…The results are shown in Table 1 , where is the length of CP. It can be seen that, compared with the matrix approximation method [ 27 ] and the singular value decomposition (SVD) method [ 29 ], the LMMSE estimator with CCF selection [ 18 ] has a relatively large amount of computation. It is evident that the amount of calculation of algorithm suggested in [ 24 ] is minimal since it only performs PDP estimation and needs no matrix inversion operations.…”

“…It is evident that the amount of calculation of algorithm suggested in [ 24 ] is minimal since it only performs PDP estimation and needs no matrix inversion operations. Compared with [ 18 ], the proposed algorithm only slightly increases the computational complexity when performing both channel LMMSE and PDP estimation.…”

“…Although these methods can effectively avoid the mismatch in frequency domain and the distortion caused by the zero subcarrier, they still cannot guarantee the optimal performance of LMMSE estimation. To solve the problem of PDP model mismatch, the LMMSE estimator with CCF selection function is considered in [ 18 ]. The selected CCF is used to construct the channel correlation matrix to avoid the performance loss.…”

“…This paper proposes an iterative joint estimation procedure for channel LMMSE and PDP. Motivated by [ 18 ], the best-matched CCF vector is selected to construct the channel correlation matrix for channel LMMSE estimation. Then, the estimated channel frequency response (CFR) is used for PDP estimation.…”