LMMSE

AcronymDefinition
LMMSELinear Minimum Mean Squared Error
References in periodicals archive ?
In order to further improve channel estimation accuracy of LMMSE algorithm criterion and reduce the complexity of the algorithm criterion, the channel response autocorrelation function [R.sub.hh] can be simplify processed by using the singular value decomposition (SVD) method.
Comparison of Different Interference Mitigation schemes on Signaling Transmission by Comp Technique using Lmmse Equalizer:
The saliency maps [[??].sub.1] and [[??].sub.2] are thus computed by applying the LMMSE based filter q and following (2):
(2) At the first step (i = 1) of the algorithm, the LMMSE channel estimation [22, 23] is performed with [[??].sup.LS.sub.H]:
Inspired by the effectiveness of LMMSE mechanic used in solving state estimation problem of MJLSs with random time delays in [33], the problem of state estimation of MJLSs with random missing observations is formulated into LMMSE filtering frame.
A major drawback of LMMSE is the complexity involved in the computation of the auto-correlation matrix.
For "SCH1", the LSE (applying (24)) and the LMMSE estimation (applying (25)) of [[??].sup.<r>] are compared.
For example, in the case of a linear minimum mean-square-error (LMMSE) filter, [mathematical expression not reproducible] is obtained as
In this section, we will demonstrate the performance of the proposed method and make a comparison with several stateof-the-art methods including NL-Mean [1], LARK [5], BLF [6], TV [8], K-SVD [15], LGM [22], and LMMSE [29].
In sensing of least minimum mean square error(LMMSE) for prediction error, the prediction parameters of the statistically optimal prediction can be expressed as
The early algorithms suppress speckles through an examination of the local statistics surrounding a given pixel using optimization criteria as the local minimum mean square error (LMMSE), for instance, the Lee filter [25], the Frost filter [26], the Kuan filter [27] and their improved version [6,13].
[3] Saqib Saleem, Qamar-ul-Islam, "Optimization of LSE and LMMSE Channel Estimation Algorithms based on CIR Samples and Channel Taps," IJCSI International Journal of Computer Science Issues, vol.