Performance enhancement of Echo Cancellation Using a Combination of Partial Update ( PU) Methods and New Variable Length LMS (NVLLMS) Algorithm

2018 ◽  
Vol 24 (5) ◽  
pp. 66
Author(s):  
Thamer M. Jamel ◽  
Faez Fawzi Hammood
Keyword(s):  
Mean Square Error ◽  
Return Loss ◽  
Performance Enhancement ◽  
Variable Length ◽  
Echo Cancellation ◽  
Convergence Time ◽  
Length Variation ◽  
Mean Square ◽  
Step Size ◽  
Echo Return Loss Enhancement

In this paper, several combination algorithms between Partial Update LMS (PU LMS) methods and previously proposed algorithm (New Variable Length LMS (NVLLMS)) have been developed. Then, the new sets of proposed algorithms were applied to an Acoustic Echo Cancellation system (AEC) in order to decrease the filter coefficients, decrease the convergence time, and enhance its performance in terms of Mean Square Error (MSE) and Echo Return Loss Enhancement (ERLE). These proposed algorithms will use the Echo Return Loss Enhancement (ERLE) to control the operation of filter's coefficient length variation. In addition, the time-varying step size is used.The total number of coefficients required was reduced by about 18% , 10% , 6%, and 16% using Periodic, Sequential, Stochastic, and M-max PU NVLLMS algorithms respectively, compared to that used by a full update method which  is very important, especially in the application of mobile communication since the power consumption must be considered. In addition, the average ERLE and average Mean Square Error (MSE) for M-max PU NVLLMS are better than other proposed algorithms.  

The Dynamic LMS for Line Echo Cancellation

2013 ◽  
Vol 284-287 ◽  
pp. 2941-2945
Author(s):  
Ning Yun Ku ◽  
Shaw Hwa Hwang ◽  
Shun Chieh Chang ◽  
Cheng Yu Yeh
Keyword(s):  
Impulse Response ◽  
Return Loss ◽  
Experimental Results ◽  
Echo Cancellation ◽  
Mean Square ◽  
Least Mean Square ◽  
Excellent Performance ◽  
Pure Delay ◽  
Echo Return Loss Enhancement ◽  
Computation Load

To the best of our knowledge, this study represents the proposal using the dynamic least mean square (DLMS) algorithm to reduce the computation load of LMS. Moreover, three regions of impulse response of line echo path are also proposed to analyze the redundant coefficients. Using the DLMS method, redundant coefficients can be detected and grouped, thereby automatically reducing computation. We employed line echo cancellation (LEC) to evaluate the performance of DLMS. The pure-delay and overlong regions of impulse response of line echo path are grouped and the associated computation load is reduced. The experimental results confirm the excellent performance of DLMS achieving a 35% savings in computation. Moreover, the quality echo return loss enhancement (ERLE) of DLMS also maintains at a level nearly equal to LMS.

scholarly journals State Estimation and Forecasting of the Ski-Slope Model Using an Improved Shadowing Filter

2016 ◽  
Vol 26 (04) ◽  
pp. 1650056
Author(s):  
Auni Aslah Mat Daud
Keyword(s):  
Root Mean Square Error ◽  
State Estimation ◽  
Mean Square Error ◽  
Gradient Descent ◽  
Slight Modification ◽  
Mean Square ◽  
Step Size ◽  
Adaptive Step Size ◽  
Adaptive Step

In this paper, we present the application of the gradient descent of indeterminism (GDI) shadowing filter to a chaotic system, that is the ski-slope model. The paper focuses on the quality of the estimated states and their usability for forecasting. One main problem is that the existing GDI shadowing filter fails to provide stability to the convergence of the root mean square error and the last point error of the ski-slope model. Furthermore, there are unexpected cases in which the better state estimates give worse forecasts than the worse state estimates. We investigate these unexpected cases in particular and show how the presence of the humps contributes to them. However, the results show that the GDI shadowing filter can successfully be applied to the ski-slope model with only slight modification, that is, by introducing the adaptive step-size to ensure the convergence of indeterminism. We investigate its advantages over fixed step-size and how it can improve the performance of our shadowing filter.

scholarly journals CMA Channel Equalization Through An Adaptive MMSE Equalizer Based RLS Algorithm

2014 ◽  
pp. 129-134
Author(s):  
Tara Saikumar ◽  
B. Smitha ◽  
P. S. Murthy
Keyword(s):  
Mean Square Error ◽  
Minimum Mean Square Error ◽  
Channel Equalization ◽  
Residual Error ◽  
Optimization Approach ◽  
Mean Square ◽  
Constant Modulus Algorithm ◽  
Step Size ◽  
Error Level ◽  
Rls Algorithm

The adaptive algorithm has been widely used in the digital signal processing like channel estimation, channel equalization, echo cancellation, and so on. One of the most important adaptive algorithms is the RLS algorithm. We present in this paper n multiple objective optimization approach to fast blind channel equalization. By investigating first the performance (mean-square error) of the standard fractionally spaced CMA (constant modulus algorithm) equalizer in the presence of noise, we show that CMA local minima exist near the minimum mean-square error (MMSE) equalizers. Consequently, CMA may converge to a local minimum corresponding to a poorly designed MMSE receiver with considerable large mean-square error. The step size in the RLS algorithm decides both the convergence speed and the residual error level, the highest speed of convergence and residual error level.

An Improved Echo Cancellation Algorithm with Low Computational Complexity

2013 ◽  
Vol 303-306 ◽  
pp. 2042-2045
Author(s):  
Ya Ting Wu ◽  
Y.Y. Zhao ◽  
Fei Yu
Keyword(s):  
Return Loss ◽  
Linear Prediction ◽  
Low Complexity ◽  
Echo Cancellation ◽  
Echo Canceller ◽  
Convergence Process ◽  
Speed Up ◽  
Echo Return Loss Enhancement ◽  
Active Detection ◽  
The Voice

A low-complexity echo canceller integrated with vocoder is proposed in this paper to speed up the convergence process. By making full use of the linear prediction parameters retrieved from decoder and the voice active detection feature of the vocoder, the new echo canceller avoids the need to calculate decorrelation filter coefficients and prewhiten the received signal separately. Simulation results show performance improvement of the proposed algorithm in terms of convergence rate and echo return loss enhancement.

Performance Enhancement of 16 Channels Back Haul DWDM OADM Ring Network Using Electronic Dispersion Compensation

2014 ◽  
Vol 513-517 ◽  
pp. 4357-4360
Author(s):  
Anurag Sharma ◽  
Vikrant Sharma ◽  
Dalvir Kaur ◽  
H.P. Singh
Keyword(s):  
Mean Square Error ◽  
Performance Enhancement ◽  
Dispersion Compensation ◽  
Minimum Mean Square Error ◽  
Mean Square ◽  
Ring Network ◽  
Eye Opening ◽  
Electronic Dispersion ◽  
Error Optimization ◽  
Inter Symbol Interference

In this paper, electronic dispersion compensation (EDC) based on minimum mean square error optimization has been employed to improve the performance of 16 channels, gigabit capacity, back haul DWDM OADM ring network.. It is observed that EDC significantly reduce BER by e-33 and improved Q2 dB by 3 dB, thus resulting in improved eye opening and considerably reduced inter-symbol interference.

Improved Blind Equalization Algorithm and Simulation

2013 ◽  
Vol 325-326 ◽  
pp. 1645-1648
Author(s):  
Wei Ju Cai
Keyword(s):  
Mean Square Error ◽  
Blind Equalization ◽  
Convergence Speed ◽  
Algorithm Analysis ◽  
Mean Square ◽  
Constant Modulus ◽  
Step Size ◽  
Equalization Algorithm ◽  
Convergence Performance ◽  
The Mean

This paper focuses on the constant modulus Busgang blind equalization algorithm (CMA blind equalization algorithm in Constant, The Modulus Algorithm). Analysis of the convergence performance of the traditional CMA blind equalization algorithm, the fixed step size, convergence speed and convergence of mutual constraint between the precision of its application under great restrictions is demonstrated in the paper. In order to solve this contradiction, this paper presents a CMA blind equalization algorithm based on the mean square error (MSE Mean Square Error).

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