scholarly journals Performance Analysis of V-Blast MIMO System Using Minimum Mean Square Error Equalizer Technique with BPSK

2018 ◽  
Vol Volume-2 (Issue-5) ◽  
pp. 310-312 ◽  
Author(s):  
Tamashri Sonartiya ◽  
Deepak Pancholi ◽  
Keyword(s):  
Performance Analysis ◽  
Mean Square Error ◽  
Mimo System ◽  
Minimum Mean Square Error ◽  
Mean Square

scholarly journals Performance Analysis for Distributed Fusion with Different Dimensional Data

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Xianghui Yuan ◽  
Zhansheng Duan ◽  
Chongzhao Han
Keyword(s):  
Performance Analysis ◽  
Mean Square Error ◽  
Cross Correlation ◽  
Minimum Mean Square Error ◽  
Mean Square ◽  
Fusion Algorithm ◽  
Common Part ◽  
Higher Dimensional ◽  
Different Dimensions ◽  
The Common

Different sensors or estimators may have different capability to provide data. Some sensors can provide a relatively higher dimensional data, while other sensors can only provide part of them. Some estimators can estimate full dimensional quantity of interest, while others may only estimate part of it due to some constraints. How is such kind of data with different dimensions fused? How do the common part and the uncommon part affect each other during fusion? To answer these questions, a fusion algorithm based on linear minimum mean-square error (LMMSE) estimation is provided in this paper. Then the fusion performance is analyzed, which is the main contribution of this work. The conclusions are as follows. First, the fused common part is not affected by the uncommon part. Second, the fused uncommon part will benefit from the common part through the cross-correlation. Finally, under certain conditions, both the more accurate common part and the stronger correlation can result in more accurate fused uncommon part. The conclusions are all supported by some tracking application examples.

scholarly journals Water-Filling Solution for Distributed Estimation of Correlated Data in WSN MIMO System

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Ajib Setyo Arifin ◽  
Tomoaki Ohtsuki
Keyword(s):  
Mean Square Error ◽  
Channel Model ◽  
Mimo System ◽  
Minimum Mean Square Error ◽  
Closed Form Solution ◽  
Distributed Estimation ◽  
Correlated Data ◽  
Multiple Access Channel ◽  
Mean Square ◽  
Water Filling

We consider the distributed estimation of a random vector signal in a power constraint wireless sensor network (WSN) that follows a multiple-input and multiple-output (MIMO) coherent multiple access channel model. We design linear coding matrices based on linear minimum mean-square error (LMMSE) fusion rule that accommodates spatial correlated data. We obtain a closed-form solution that follows a water-filling strategy. We also derive a lower bound to this model. Simulation results show that when the data is more correlated, the distortion in terms of mean-square error (MSE) degrades. By taking into account the effects of correlation, observation, and channel matrices, the proposed method performs better than equal power method.

Performance analysis of global local mean square error criterion of stochastic linearization for nonlinear oscillator

2019 ◽  
Author(s):  
Nguyen Cao Thang ◽  
Luu Xuan Hung
Keyword(s):  
Performance Analysis ◽  
Mean Square Error ◽  
Nonlinear Oscillators ◽  
Degree Of Freedom ◽  
Equivalent Linearization ◽  
Mean Square ◽  
Error Criterion ◽  
Stochastic Linearization ◽  
Local Mean ◽  
Mean Square Error Criterion

The paper presents a performance analysis of global-local mean square error criterion of stochastic linearization for some nonlinear oscillators. This criterion of stochastic linearization for nonlinear oscillators bases on dual conception to the local mean square error criterion (LOMSEC). The algorithm is generally built to multi degree of freedom (MDOF) nonlinear oscillators. Then, the performance analysis is carried out for two applications which comprise a rolling ship oscillation and two degree of freedom one. The improvement on accuracy of the proposed criterion has been shown in comparison with the conventional Gaussian equivalent linearization (GEL).

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