Non-central Chi-squared Distribution
If squares of k independent standard normal random variables (mean=0, variance=1) are added, it gives rise to central Chi-squared distribution with ‘k’ degrees of freedom. Instead, if squares of k independent normal random variables with non-zero mean (mean 0 , variance=1) are added, it gives rise to non-central Chi-squared distribution.
The non-central Chi-squared distribution is a generalization of Chi-square distribution. A non-central Chi squared distribution is defined by two parameters: 1) degrees of freedom and 2) non-centrality parameter.
As we know from previous article, the degrees of freedom specify the number of independent random variables we want to square and sum-up to make the Chi-squared distribution. Non-centrality parameter is the sum of squares of means of the each independent underlying Normal random variable.
The non-centrality parameter is given by
The PDF of the non-central Chi-squared distribution is given by
In the above equation, indicates the non-central Chi-squared distribution with k degrees of freedom with non-centrality parameter specified by and the factor indicates the ordinary central Chi-squared distribution with k+2n degrees of freedom.
The factor gives the probabilities of Poisson Distribution. So, the PDF of the non-central Chi-squared distribution can be termed as a weighted sum of Chi-squared probability with weights being equal to the probabilities of Poisson distribution.
Method of Generating non-central Chi-squared random variable:
Parameters required: k – the degrees of freedom and – non-centrality parameter.
- For a given degree of freedom (k), let the k normal random variables be with variances and mean
- Now, our goal is to add squares of the k independent normal random variables with variances=1 and means satisfying the following criteria
- Put and
- Generate k-1 standard normal random variables with and and one normal random variable with and
- Squaring and summing-up all the k random variables give the non-central Chi-squared random variable
- The PDF can be plotted using histogram method
Check this book for full Matlab code.
Simulation of Digital Communication Systems Using Matlab – by Mathuranathan Viswanathan
 Introduction to Random Variables, PDF and CDF
 Central Chi-squared distribution and its simulation in Matlab
 Uniform Random Variables and Uniform Distribution
 Derivation of Error Rate Performance of an optimum BPSK receiver in AWGN channel
 Eb/N0 Vs BER for BPSK over Rician Fading Channel
 BER Vs Eb/N0 for QPSK modulation over AWGN
 BER Vs Eb/N0 for 8-PSK modulation over AWGN
 Simulation of M-PSK modulation techniques in AWGN channel
 Performance comparison of Digital Modulation techniques
 Chi-Square Test Penn State University
 Java Applet – Chi Square goodness of Fit test – created by David Eck and modified by Jim Ryan – Mathbeans project
 Chi-Square Test for variance ,e-handbook of statistical methods,National Institute of Standards and Technology
 Dr. Claude Moore,Estimation of Variance,Cape Fear Community College