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

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, 30th November 2014 at 19:37 (8354 Views)

In Part I of this blog, we saw that we can construct two partially correlated signals, x_{1}(t) and x_{2}(t), as:

where P_{1}and P_{2}are the desired signal powers andρis their desired cross-correlation. s_{1}(t) and s_{2}(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 x_{2}(t), we can see that (loosely speaking) it has been designed in two parts: the s_{1}(t) part which correlates with x_{1}(t) and the s_{2}(t) part which is uncorrelated with x_{1}(t).

So, if we wanted to design a third signal, x_{3}(t), then we would need a third uncorrelated signal, s_{3}(t), in order to freely choose correlations with x_{1}(t) and x_{2}(t). More generally, we expect then^{th}signal, x_{n}(t), to be a function ofnuncorrelated signals, s_{1}(t), s_{2}(t), ... , s_{n}(t). We can write this idea in matrix form:

or, defining a variable for each bracketed term:

whereAis 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:

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

To clarify, let's say we want our signals to have covariance matrixR_{xx}:

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:

whereLis a lower triangular matrix. We can rewrite this as:

whereIis the (N x N) identity matrix. So, by comparing this to Equation (18), we can clearly defineA=Lin Equation (17), so that:

There are several methods for obtainingLin 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 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 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:

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.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

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 notethat 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 2 3 4 // 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]);