Improved Constant Modulus Algorithm for Blind Equalization of QAM Signals

2014 ◽  
Vol 631-632 ◽  
pp. 824-829
Author(s):  
Emmanuel Anania Mwangosi ◽  
Cang Yan ◽  
Naveed Ur Rehman
Keyword(s):  
Steady State ◽  
Cost Function ◽  
Error Function ◽  
Constant Modulus ◽  
Constant Modulus Algorithm ◽  
Error Performance ◽  
New Approach ◽  
Slow Convergence ◽  
The Cost ◽  
State Error

The paper present new approach for improving the steady-state error performance of Constant Modulus Algorithm (CMA), it is well known that for higher level modulations such as QAM, CMA does not perform well. Several techniques have been proposed in recent years to deal with slow convergence and MSE performance of CMA. Constellation matched error has been seen to offer best performance by providing the cost function with the knowledge of the constellation symbols. New constellation match error function is studied, simulation is performed, it can be witnessed that 4dB improvement stead state error performance.

Blind Equalization Algorithm Based on the Orthogonal Wavelet Transform for SIMO Systems

2012 ◽  
Vol 198-199 ◽  
pp. 1493-1496
Author(s):  
Zhen Wang ◽  
Ye Cai Guo
Keyword(s):  
Wavelet Transform ◽  
Steady State ◽  
Blind Equalization ◽  
Constant Modulus ◽  
Constant Modulus Algorithm ◽  
Orthogonal Wavelet ◽  
Equalization Algorithm ◽  
Single Input ◽  
Orthogonal Wavelet Transform ◽  
State Error

In order to improve the equalization effects of the constant modulus blind equalization algorithm (CMA) for Single-Input and Multiple-Output (SIMO) systems, orthogonal wavelet transform constant modulus algorithm (WT-CMA) based on SIMO is proposed. This proposed algorithm uses the orthogonal wavelet transform to decrease the autocorrelation of the input signals to accelerate the convergence rate and reduce the steady-state error. Theoretical analysis and computer simulations shows that the proposed algorithm has better performance and smaller steady-state error in SIMO systems, it is very easy to achieve in engineering.

PREDICTION OF MUSCLE FORCES USING STATIC OPTIMIZATION FOR DIFFERENT CONTRACTILE CONDITIONS

2013 ◽  
Vol 13 (03) ◽  
pp. 1350022 ◽  
Author(s):  
YUNUS ZIYA ARSLAN ◽  
AZIM JINHA ◽  
MOTOSHI KAYA ◽  
WALTER HERZOG
Keyword(s):  
Cost Function ◽  
Fiber Type ◽  
Degree Of Freedom ◽  
Muscle Forces ◽  
Cross Sectional ◽  
Satisfactory Description ◽  
New Approach ◽  
Force Sharing ◽  
Synergistic Muscles ◽  
The Cost

In this study, we introduced a novel cost function for the prediction of individual muscle forces for a one degree-of-freedom musculoskeletal system. Unlike previous models, the new approach incorporates the instantaneous contractile conditions represented by the force-length and force-velocity relationships and accounts for physiological properties such as fiber type distribution and physiological cross-sectional area (PCSA) in the cost function. Using this cost function, it is possible to predict experimentally observed features of force-sharing among synergistic muscles that cannot be predicted using the classical approaches. Specifically, the new approach allows for predictions of force-sharing loops of agonistic muscles in one degree-of-freedom systems and for simultaneous increases in force in one muscle and decreases in a corresponding agonist. We concluded that the incorporation of the contractile conditions in the weighting of cost functions provides a natural way to incorporate observed force-sharing features in synergistic muscles that have eluded satisfactory description.

scholarly journals New approach to the retrieval of AOD and its uncertainty from MISR observations over dark water

2018 ◽  
Vol 11 (1) ◽  
pp. 429-439 ◽  
Author(s):  
Marcin L. Witek ◽  
Michael J. Garay ◽  
David J. Diner ◽  
Michael A. Bull ◽  
Felix C. Seidel
Keyword(s):  
Statistical Analysis ◽  
Cost Function ◽  
Entire Range ◽  
Cost Functions ◽  
Retrieval Algorithm ◽  
New Approach ◽  
Aerosol Retrieval ◽  
Operational Algorithm ◽  
Confidence Index ◽  
The Cost

Abstract. A new method for retrieving aerosol optical depth (AOD) and its uncertainty from Multi-angle Imaging SpectroRadiometer (MISR) observations over dark water is outlined. MISR's aerosol retrieval algorithm calculates cost functions between observed and pre-simulated radiances for a range of AODs (from 0.0 to 3.0) and a prescribed set of aerosol mixtures. The previous version 22 (V22) operational algorithm considered only the AOD that minimized the cost function for each aerosol mixture and then used a combination of these values to compute the final, “best estimate” AOD and associated uncertainty. The new approach considers the entire range of cost functions associated with each aerosol mixture. The uncertainty of the reported AOD depends on a combination of (a) the absolute values of the cost functions for each aerosol mixture, (b) the widths of the cost function distributions as a function of AOD, and (c) the spread of the cost function distributions among the ensemble of mixtures. A key benefit of the new approach is that, unlike the V22 algorithm, it does not rely on empirical thresholds imposed on the cost function to determine the success or failure of a particular mixture. Furthermore, a new aerosol retrieval confidence index (ARCI) is established that can be used to screen high-AOD retrieval blunders caused by cloud contamination or other factors. Requiring ARCI ≥0.15 as a condition for retrieval success is supported through statistical analysis and outperforms the thresholds used in the V22 algorithm. The described changes to the MISR dark water algorithm will become operational in the new MISR aerosol product (V23), planned for release in 2017.

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 Dual-Mode FSE Suitable for High-Order QAM Signals

2013 ◽  
Vol 760-762 ◽  
pp. 478-482
Author(s):  
Hui Juan Gao ◽  
Yan Liu
Keyword(s):  
Steady State ◽  
Mean Square Error ◽  
Digital Transmission ◽  
High Order ◽  
Mean Square ◽  
Constant Modulus ◽  
Constant Modulus Algorithm ◽  
Dual Mode ◽  
Training Sequences ◽  
Second Mode

In the digital transmission system, constant modulus algorithm (CMA) is a famous blind equalization to overcome the inter-symbol interference without the aid of training sequences. But for the non-constant modulus signals such as higher-order QAM signals, the CMA just achieve moderate steady-state mean square error (MSE). So a new dual-mode fractionally-spaced equalization (FSE) suitable for high-order QAM signals is proposed, which makes full use of the character which is that the high-order QAM signals have the different modulus. This algorithm uses the FSE based on CMA as the basal mode and in the second mode it uses the FSE based on variable modulus algorithm. The simulation results show that compared with CMA the proposed algorithm has faster convergence rate and lower steady-state mean square error.

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