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...

Longitudinal surveys are very common in sampling over interval of times to estimate the aggregate level of population means at given point of time. In the present paper, the estimation methods have been developed for small area in longitudinal surveys using small area estimation (SAE) techniques. Direct, synthetic and composite estimators have been proposed to estimate the population mean of sm...

In this paper, a variable step size based least mean squares (LMS) channel estimation (CE) algorithm is presented for a single carrier frequency division multiple access (SC-FDMA) system under the umbrella of the long term evolution (LTE). This unbiased CE method can automatically adapt the weighting coefficients on the channel condition. Therefore, it does not require knowledge of channel, and...

This paper studies the joint digital self-interference (SI) cancellation and data detection in an orthogonal-frequency-division-multiplexing (OFDM) full-duplex (FD) system, considering effect of phase noise introduced by oscillators at both local transmitter receiver. In particular, universal iterative two-stage SI framework is considered its performance bound independent any specific estimatio...

This paper proposes a fast block-matching algorithm that uses three fast matching error measures, besides the conventional mean-absolute error (MAE) or mean-square error (MSE). An incoming reference block in the current frame is compared to candidate blocks within the search window using multiple matching criteria. These three fast matching error measures are established on the integral project...

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...

In this brief, we develop an least mean square (LMS) algorithm that converge in a statistical sense to the global minimum of the mean square error (MSE) objective function. This is accomplished by estimating the gradient as a smoothed version of the MSE. The smoothed MSE objective begins as a convex functional in the mean. The amount of dispersion or smoothing is reduced, such that over time it...

Recently, sparse adaptive learning algorithms have been developed to exploit system sparsity as well as to mitigate various noise disturbances in many applications. In particular, in sparse channel estimation, the parameter vector with sparsity characteristic can be well estimated from noisy measurements through a sparse adaptive filter. In previous studies, most works use the mean square error...