Analysis on the Empirical Spectral Distribution of Large Sample Covariance Matrix and Applications for Large Antenna Array Processing

Lu, Guanping; Wu, Jinsong; Qiu, Robert C.; Ling, Hong

doi:10.1109/access.2019.2902039

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…The Gaussian distribution of x is represented by: 𝑿 ͠ 𝑵 (µ ,σ) (5) it is clear that, there are two parameters characterized the Gaussian (pdf), which are(µ , 𝜎) that represent the first and second order moment respectively and can be defined by: [23,24]:…”

Section: Methodsmentioning

confidence: 99%

“…As shown in Figure (2), the mean value (µ )have the highest value ,while the other elements (µ − 𝜎, µ − 2𝜎, µ + 𝜎, µ + 2𝜎) have smallest value,so its weight will be small depending on the value of Gaussian function, so the features will be selected in more suitable manner by giving the mean value the highest weight and the weight is decreased as moving from the mean according to the significance of the statistics, as shown in Figure 2, ( µ + 𝜎) and µ − 𝜎) have weight smaller than µ , also ( µ + 2𝜎) and µ − 2𝜎) have lowest values, this can increase the obtained information from using the mean only. The other contributions of this method (GWT) is proposed feature selection method based on statistics of each pool as described in the different Gaussian function in Figure 2, which is described the differences between there different Gaussian function values according to µ 𝑎𝑛𝑑 𝜎 [24][25][26].…”

Section: Methodsmentioning

confidence: 99%

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A novel pooling layer based on gaussian function with wavelet transform

alhussainy

Jasim

2020

IJEECS

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<p>Convolution is represent the basic layer in the convolutional neural network, but it can results in very big size of the data at its output, so this data need to be reduced or down sampled to reduce the required operation in the network and increase its efficiency by choosing the most important features of the input signal. Different pooling methods was used to perform down sampling the size of these features. In this paper, we have proposed a novel pooling method by using Gaussian based Wavelet Transform and named as (GWT). Gaussian probability distribution function is used to determine the basic and most important statistics of the signal in each pooling size, and depending on this function, the basic statistics with the most priority is determined with their weight ,which are used as coefficients for wavelet filter to extract the pooling features . According to the procedure of extract the wavelet coefficients filter, Three method are proposed ,the first method (GWT1 ) is used the normalized values of basic statistics as weight to be multiplied by original signal, the second method (GWT2) used the determined statistics as features of the original signal and multiply it with constant weights based on half Gaussian ,while the third method (GWT3) is work in similar way to (GWT1) except that, it depend on entire signal instead of every pool size for calculation the basic statistics. Also the proposed methods are combined with other standard methods such as max and pooling. The experiments are performed on different types of dataset , two dimension (images) and one dimension (such as ECG signal), the results are compared with other methods and show that the proposed methods perform or outperform the other methods and can increase the performance of the (CNN).</p>

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A novel pooling layer based on gaussian function with wavelet transform

alhussainy

Jasim

2020

IJEECS

View full text Add to dashboard Cite

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Predicted encounter probability based on dynamic programming proposed probability algorithm in opportunistic social network

Chen

2020

Computer Networks

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Efficient Matrix Polynomial Expansion Detector for Large-Scale MIMO: An Inverse-Transform-Sampling Approach

Deng

Liang

et al. 2023

IEEE Systems Journal

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Matrix polynomial expansion (MPE) based detector incurs either complicated computation of polynomial coefficients or slow convergence in uplink large-scale MIMO (LS-MIMO) systems. To solve these issues, an improved MPE (IMPE) detector is proposed, which can speed up the convergence significantly with uncomplicated polynomial coefficients. However, a challenging problem of performing IMPE is needed to compute all the eigenvalues of channel covariance matrix in real time. Unfortunately, directly calculating the eigenvalues of the channel covariance matrix requires complexity, which is as costly as the matrix inverse. To this end, an inverse-transform-sampling based IMPE (ITS-IMPE) detector is proposed to enhance the convergence rate and accuracy in a simple way. First, the closedform expression of the eigenvalue spectral cumulative distribution function of the channel covariance matrix is deduced analytically, which is a critical factor that influence the eigenvalues estimation. Second, the improved polynomial coefficients of ITS-IMPE are then introduced by a fast online ITS-based eigenvalues estimation algorithm and a least-squares fitting procedure, which achieve a well trade-off between precision and computation. Simulation results exhibit that ITS-IMPE detector is able to achieve a significant enhancement performance with much lower complexity compared with many reported detectors under Rayleigh fading channel and low spatial correlated channel.

show abstract

Analysis on the Empirical Spectral Distribution of Large Sample Covariance Matrix and Applications for Large Antenna Array Processing

Cited by 4 publications

References 10 publications

A novel pooling layer based on gaussian function with wavelet transform

A novel pooling layer based on gaussian function with wavelet transform

Predicted encounter probability based on dynamic programming proposed probability algorithm in opportunistic social network

Efficient Matrix Polynomial Expansion Detector for Large-Scale MIMO: An Inverse-Transform-Sampling Approach

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