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).

An Optimized Combination of Frequency Domain Adaptive Equalizers

2014 ◽  
Vol 548-549 ◽  
pp. 766-770
Author(s):  
Ke Cheng Leng ◽  
Cheng Bie ◽  
Xi Gong ◽  
Ran Xu ◽  
Ye Cai Guo
Keyword(s):  
Frequency Domain ◽  
Mean Square Error ◽  
Time Domain ◽  
Blind Equalization ◽  
Mean Square ◽  
Constant Modulus ◽  
Equalization Algorithm ◽  
Adaptive Equalizers ◽  
Simulation Results ◽  
The Time Domain

In order to overcome the defects of the high computational loads and selecting the threshold of mean square error (MSE) for time domain decision-directed constant modulus blind equalization algorithm (DD+CMA), a frequency domain parallel decision multi-modulus blind equalization algorithm based on frequency domain MMA(FMMA) and frequency domain LMS (FLMS) algorithm is proposed. The proposed algorithm is composed of the FMMA and FLMS, and the FMMA and FLMS run automatically in soft switching parallel manner. In running process, it is not necessary to selecting the threshold of the MSE. Moreover, the computational loads can be reduced by circular convolution in the frequency domain signals instead of linear one of the time domain signals. Simulation results show that performance of the proposed algorithm outperforms the FLMS and the FMMA algorithm.

Momentum Factor Constant Modulus Algorithm and Theory

2012 ◽  
Vol 263-266 ◽  
pp. 1058-1061
Author(s):  
Heng Yang ◽  
Jing Wang ◽  
Jing Guan ◽  
Wei Lu
Keyword(s):  
Theoretical Analysis ◽  
Blind Equalization ◽  
Weight Vector ◽  
Convergence Speed ◽  
Constant Modulus ◽  
Constant Modulus Algorithm ◽  
Slow Convergence ◽  
Iteration Formula ◽  
Equalization Algorithm ◽  
Factor Constant

The traditional constant modulus algorithm (CMA) has the disadvantage of slow convergence in blind equalization algorithm. This paper studied one improved algorithm based on momentum factor constant modulus algorithm(MCMA) to solve this problem, momentum factor was added to the weight vector iteration formula of CMA to improve the convergence speed. theoretical analysis and simulation showed that: in the case of the same equalization effect, the MCMA converges faster than the traditional constant modulus algorithm, and also different momentum factors have different convergence effects. The larger the momentum factor , the better convergence effect in the defined domain of the momentum factor.

New Concurrent Fractionally-Spaced Blind Equalization

2010 ◽  
Vol 108-111 ◽  
pp. 363-368 ◽  
Author(s):  
Wei Rao ◽  
Ye Cai Guo ◽  
Min Chen ◽  
Wen Qun Tan ◽  
Jian Bing Liu ◽  
...  
Keyword(s):  
Steady State ◽  
Convergence Rate ◽  
Mean Square Error ◽  
Blind Equalization ◽  
Mean Square ◽  
Constant Modulus ◽  
Constant Modulus Algorithm ◽  
Underwater Acoustic ◽  
Underwater Acoustic Channels ◽  
Simulation Results

The paper proposes a concurrent constant modulus algorithm (CMA) and decision-directed (DD) scheme for fractionally-spaced blind equalization. The proposed algorithm makes full use of the advantages of CMA and DD algorithm. A novel rule to control the adjustment of DD’s tap weights vector is proposed which avoids the hard switch between CMA and DD in practice. Simulations with underwater acoustic channels are used to compare the proposed algorithm with the famous CMA. And the simulation results show that the proposed algorithm has faster convergence rate and lower steady state mean square error.

New Hybrid Blind Equalization Algorithms

2012 ◽  
Vol 182-183 ◽  
pp. 1810-1815
Author(s):  
Shun Lan Liu ◽  
Lin Wang
Keyword(s):  
Blind Equalization ◽  
Convergence Speed ◽  
Variable Step Size ◽  
Constant Modulus ◽  
Computation Complexity ◽  
Constant Modulus Algorithm ◽  
Step Size ◽  
Variable Step ◽  
Simulation Results ◽  
The Cost

A novel decision-directed Modified Constant Modulus Algorithm (DD-MCMA) was proposed firstly. Then a constellation matched error (CME) function was added to the cost function of DD-MCMA and CME-DD-MCMA algorithm was presented. Furthermore, we improve the CME-DD-MCMA by replacing the fixed step with variable step size, that is VSS-CME-DD-MCMA algorithm. The simulation results show that the proposed new blind equalization algorithms can tremendously accelerate the convergence speed and achieve lower residual inter-symbol interference (ISI) than MCMA, and among the three proposed algorithms, VSS-CME-DD-MCMA has the fastest convergence speed and the lowest residual ISI, but it has the largest computation complexity.

The Bordeaux Automatic Meridian Circle

1978 ◽  
Vol 48 ◽  
pp. 227-228
Author(s):  
Y. Requième
Keyword(s):  
Mean Square Error ◽  
Mean Square ◽  
Meridian Circle ◽  
The Mean ◽  
Full Automation

In spite of important delays in the initial planning, the full automation of the Bordeaux meridian circle is progressing well and will be ready for regular observations by the middle of the next year. It is expected that the mean square error for one observation will be about ±0.”10 in the two coordinates for declinations up to 87°.

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.  

Limit tolerances for sums of measurement errors

2018 ◽  
Vol 934 (4) ◽  
pp. 59-62
Author(s):  
V.I. Salnikov
Keyword(s):  
Normal Distribution ◽  
Mean Square Error ◽  
Gaussian Distribution ◽  
Measurement Errors ◽  
Theory And Practice ◽  
Mean Square ◽  
The Mean ◽  
Limiting Values ◽  
Limiting Value

The question of calculating the limiting values of residuals in geodesic constructions is considered in the case when the limiting value for measurement errors is assumed equal to 3m, ie ∆рred = 3m, where m is the mean square error of the measurement. Larger errors are rejected. At present, the limiting value for the residual is calculated by the formula 3m√n, where n is the number of measurements. The article draws attention to two contradictions between theory and practice arising from the use of this formula. First, the formula is derived from the classical law of the normal Gaussian distribution, and it is applied to the truncated law of the normal distribution. And, secondly, as shown in [1], when ∆рred = 2m, the sums of errors naturally take the value equal to ?pred, after which the number of errors in the sum starts anew. This article establishes its validity for ∆рred = 3m. A table of comparative values of the tolerances valid and recommended for more stringent ones is given. The article gives a graph of applied and recommended tolerances for ∆рred = 3m.

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