# weetabixharry

#### Creating Partially Correlated Random Variables: Part II

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by , 30th November 2014 at 19:37 (8321 Views)

In Part I of this blog, we saw that we can construct two partially correlated signals, x1(t) and x2(t), as:

$3 \;\;\;\;\;\;\;\;\;\;\;\;\;x_1(t)=\sqrt{P_1}s_1(t)\\ \;\;\;\;\;\;\;\;\;\;\;\;\;x_2(t)=\sqrt{P_2}\(\rho^*s_1(t)+\sqrt{(1-|\rho|^2)}s_2(t)\)$

where P1 and P2 are the desired signal powers and ρ is their desired cross-correlation. s1(t) and s2(t) are uncorrelated signals with zero mean and unit power.

We will now generalise this two-signal concept to N signals.

Generalising to N Correlated Signals

By inspecting our expression for x2(t), we can see that (loosely speaking) it has been designed in two parts: the s1(t) part which correlates with x1(t) and the s2(t) part which is uncorrelated with x1(t).

So, if we wanted to design a third signal, x3(t), then we would need a third uncorrelated signal, s3(t), in order to freely choose correlations with x1(t) and x2(t). More generally, we expect the nth signal, xn(t), to be a function of n uncorrelated signals, s1(t), s2(t), ... , sn(t). We can write this idea in matrix form:

$3 \left[ \begin{array}{c} x_{1}(t)\\ x_{2}(t)\\ x_{3}(t)\\ \vdots\\ x_{N}(t) \end{array} \right]=\left[ \begin{array}{ccccc} a_{1,1}&&&&0\\ a_{2,1}&a_{2,2}&&&\\ a_{3,1}&a_{3,2}&\ddots&&\\ \vdots&\vdots&\ddots&\ddots&\\ a_{N,1}&a_{N,2}&\cdots&a_{N,(N-1)}&a_{N,N} \end{array} \right]\left[ \begin{array}{c} s_{1}(t)\\ s_{2}(t)\\ s_{3}(t)\\ \vdots\\ s_{N}(t) \end{array} \right]$

or, defining a variable for each bracketed term:

$3 \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\underline{x}(t)=\mathbf{A}\underline{s}(t)\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;(17)$

where A is a lower triangular matrix.

So, for example, in the 2-signal case (i.e. N = 2), we showed in Part I of this blog that:

$3 \left[ \begin{array}{c} x_{1}(t)\\ x_{2}(t) \end{array} \right] =\left[ \begin{array}{cc} \sqrt{P_{1}}&0\\ \sqrt{P_{2}}\rho^*\;&\sqrt{P_{2}(1-|\rho|^{2})} \end{array} \right]\left[ \begin{array}{c} s_{1}(t)\\ s_{2}(t) \end{array} \right]$

But how do we find the values of the lower triangular matrix, A, in general? The answer is the Cholesky decomposition of the desired theoretical covariance matrix.

To clarify, let's say we want our signals to have covariance matrix Rxx:

$3 \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\mathbf{R}_{xx}=\mathcal{E}\{\underline{x}(t)\underline{x}^H(t)\}\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;(18)$

where (.)H = ((.)*)T denotes the conjugate transpose. Then, since all covariance matrices are positive semidefinite, there must exist a lower triangular matrix, L, such that:
$3 \mathbf{R}_{xx}=\mathbf{L}\mathbf{L}^H$

where L is a lower triangular matrix. We can rewrite this as:

$3 \mathbf{R}_{xx}=\mathbf{L}\mathbf{I}\mathbf{L}^H\\ \;\;\;\;\;\;=\mathbf{L}\mathcal{E}\{\underline{s}(t)\underline{s}^H(t)\}\mathbf{L}^H$

where I is the (N x N) identity matrix. So, by comparing this to Equation (18), we can clearly define A = L in Equation (17), so that:

$3 \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\underline{x}(t)=\mathbf{L}\underline{s}(t)\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;(19)$

There are several methods for obtaining L in practice. I will first give a full Matlab example, before describing how to compute the Cholesky decomposition of a complex matrix using any programming language you want.

Matlab Example

```Code Matlab M - [expand]1
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close all; clear all; clc;

% Number of samples to generate
Nsamps = 100000;

% Number of signals
N = 4;

% Set the required second order statistics
P1 = 2.3;
P2 = 0.75;
P3 = 3.4;
P4 = 1.23;
rho12 = 0.2 - 1j*0.3;
rho13 = -0.6 + 1j*0.1;
rho14 = -1j*0.4;
rho23 = 0.1 + 1j*0.1;
rho24 = 0.5;
rho34 = -0.3 - 1j*0.1;

% 4x4 (theoretical) covariance matrix
Rxx = [P1,                      sqrt(P1*P2)*rho12,       sqrt(P1*P3)*rho13,       sqrt(P1*P4)*rho14;
sqrt(P2*P1)*conj(rho12), P2,                      sqrt(P2*P3)*rho23,       sqrt(P2*P4)*rho24;
sqrt(P3*P1)*conj(rho13), sqrt(P3*P2)*conj(rho23), P3,                      sqrt(P3*P4)*rho34;
sqrt(P4*P1)*conj(rho14), sqrt(P4*P2)*conj(rho24), sqrt(P4*P3)*conj(rho34), P4]

% Generate zero-mean, unit-power, uncorrelated Gaussian random signals
s = randn(N, Nsamps);

% Create partially correlated signals
L = chol(Rxx,'lower');
X = L*randn(N,Nsamps);

% 4x4 (practical) sample covariance matrix
Rxx_samp = X*X' / Nsamps```

The above code will produce an output similar to the following:

Code:
```Rxx =

2.3000 + 0.0000i   0.2627 - 0.3940i  -1.6779 + 0.2796i   0.0000 - 0.6728i
0.2627 + 0.3940i   0.7500 + 0.0000i   0.1597 + 0.1597i   0.4802 + 0.0000i
-1.6779 - 0.2796i   0.1597 - 0.1597i   3.4000 + 0.0000i  -0.6135 - 0.2045i
0.0000 + 0.6728i   0.4802 + 0.0000i  -0.6135 + 0.2045i   1.2300 + 0.0000i

Rxx_samp =

2.2921 + 0.0000i   0.2602 - 0.3927i  -1.6626 + 0.2777i  -0.0055 - 0.6739i
0.2602 + 0.3927i   0.7513 + 0.0000i   0.1649 + 0.1632i   0.4805 - 0.0006i
-1.6626 - 0.2777i   0.1649 - 0.1632i   3.3858 + 0.0000i  -0.6106 - 0.2066i
-0.0055 + 0.6739i   0.4805 + 0.0006i  -0.6106 + 0.2066i   1.2347 + 0.0000i```
As in the 2-signal case, note that we never expect the sample covariance matrix to exactly match the theoretical covariance matrix. Sample statistics are themselves random variables, having their own probability distributions.

Other Programming Languages

Free source code is provided in 37 languages for computing the Cholesky decomposition of real matrices at this Rosetta Code page: [link]. Please note that many of these implementations need a very minor modification to work with complex-valued matrices.

Specifically, one term needs conjugating. Using the C code example as pseudocode, we need to conceptually modify the following:

```Code C++ - [expand]1
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// Replace this operation...
s += L[i * n + k] * L[j * n + k];
// ... with this operation
s += L[i * n + k] * conj(L[j * n + k]);```
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