In the present paper we compute the geometric minimum mean square error for the vector linear estimation problem. We do this by proving that the vector linear estimator that minimizes the mean square error (MSE) also minimizes the geometric MSE.

Sparse system identification has received a great deal of attention due to its broad applicability. The proportionate normalized least mean square (PNLMS) algorithm, as a popular tool, achieves excellent performance for sparse system identification. In previous studies, most of the cost functions used in proportionate-type sparse adaptive algorithms are based on the mean square error (MSE) crit...

We study the effect of fading in the communication channels between nodes on the performance of the incremental least mean square (ILMS) algorithm. We derive steadystate performance metrics, including the mean-square deviation (MSD), excess mean-square error (EMSE), and mean-square error (MSE). We obtain the sufficient conditions to ensure meansquare convergence, and verify our results through ...

We consider the problem of signal estimation (denoising) from a statistical-mechanical perspective, in continuation to a recent work on the analysis of mean-square error (MSE) estimation using a direct relationship between optimum estimation and certain partition functions. The paper consists of essentially two parts. In the first part, using the aforementioned relationship, we derive single-le...

This paper defines haw the quality of approximation used in the error transfer function analysis affects the accuracy of the resulting steady-state mean square error (MSE) of the Least-Mean-Square (LMS) adaptive filters. It shows that the error transfer function approach is very accurate for extremely narrow bandwidth input signals, and it deteriorates as the bandwidth of input signal increases.

In this paper, the Acoustic Echo Cancellation (AEC) are investigated by using Finite Impulse Responses Adaptive Filter with the analysis of Mean Square Error (MSE) and its convergence property. It is the result of a project in the course Fundamental of Signal Processing at Chongqing University of Posts and Telecommunications. It focuses on Normalized Least Mean Square (NLMS) algorithm of adapti...

This paper addresses Automatic Blind Modulation Recognition (ABMR)problem, utilizing a Mean Square Error (MSE) decision rule to recognize and differentiate M-ary PSK modulated signals in presence of noise and fading. The performance of the modulation recognition scheme has been evaluated by simulating different types of PSK signals. By putting appropriate Mean Square Error Difference Threshold ...

In this paper, we consider the steady state Mean Square Error (MSE) analysis for 2-D LMS algorithm in which the filter's weights are updated in both vertical and horizontal directions using Fornasini and Marchesini (F-M) state space model. The MSE analysis is conducted using the wellknown independence assumption. First we show that computation of the Weight-Error Correlation Matrix (WECM) for F...