The joint probability density function of the SSC combiner output signal at two time instants in the presence of Rayleigh fading

2007 ◽  
Author(s):  
M.C. Stefanovic ◽  
D.S. Krstic ◽  
M. Petrovic ◽  
D. Bandjurt
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Output Signal ◽  
Rayleigh Fading ◽  
Joint Probability ◽  
Joint Probability Density Function ◽  
Joint Probability Density

The Joint Probability Density Function of the SSC Combiner Output Signal at two Time Instants in the Presence of Log-Normal Fading

1970 ◽  
Vol 109 (3) ◽  
pp. 11-16 ◽  
Author(s):  
D. S. Krstic ◽  
P. B. Nikolic ◽  
M. C. Stefanovic ◽  
F. Destovic
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Output Signal ◽  
Joint Probability ◽  
Time Instant ◽  
Joint Probability Density Function ◽  
Decision Threshold ◽  
Log Normal ◽  
Joint Probability Density

In this paper the probability density function of the Switch and Stay Combiner (SSC) output signal at one time instant and the joint probability density function of the SSC combiner output signal at two time instants, in the presence of log-normal fading, are determined in the closed form expressions. The results are shown graphically for different variance values and decision threshold values. If the digital telecommunication systems work on the manner described in this paper, the error probability will be significantly reduced. Ill. 6, bibl. 24 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.109.3.161

scholarly journals Relationship Between Kendall's tau Correlation and Mutual Information

2020 ◽  
Vol 43 (1) ◽  
pp. 3-20
Author(s):  
Mohammad Bolbolian Ghalibaf
Keyword(s):  
Probability Density Function ◽  
Mutual Information ◽  
Probability Density ◽  
Density Function ◽  
Joint Probability ◽  
Joint Probability Density Function ◽  
Kendall’S Tau ◽  
Kendall's Tau ◽  
Joint Histogram ◽  
Joint Probability Density

Mutual information (MI) can be viewed as a measure of multivariate association in a random vector. However, the estimation of MI is difficult since the estimation of the joint probability density function (PDF) of non Gaussian distributed data is a hard problem. Copula function is an appropriate tool for estimating MI since the joint probability density function ofrandom variables can be expressed as the product of the associated copula density function and marginal PDF’s. With a little search, we find that the proposed copulas-based mutual information is much more accurate than conventional methods such as the joint histogram and Parzen window-based MI. In this paper, by using the copulas-based method, we compute MI forsome family of bivariate distribution functions and study the relationship between Kendall’s tau correlation and MI of bivariate distributions. Finally, using a real dataset, we illustrate the efficiency of this approach.

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