Index-> contents reference index search Up-> SPT_HELP SignalGeneratorMain cawgn Prev Next SPT_HELP-> SPTFunctionsByCategory Mathematical Functions Data Manipulation Functions SignalGeneratorMain AnalogFilterFunctions FIR Filter Design Window Functions IIR Filter Design FourierFunctions Plotting Functions Histogram Functions SignalGeneratorMain-> binbits nrzbits awgn cawgn ammod pmmod fmmod quadmod sinwave triwave sawwave sqrwave RandNumGens OtherSigGen cawgn Headings-> Description Example

 Syntax `y = cawgn(means,sigmas)` Syntax `y = cawgn(means,sigmas,Nrows)` Syntax `y = cawgn(means,sigmas,Nrows,Mcols)` Include: `include spt\cawgn.oms` See Also awgn
``` ARGUMENTS:    INPUTS:       means  = VECTOR, 2-element, any numerical type. Mean                values of the real and imaginary parts of                the requested distribution. means(1)=real                part mean, means(2) = imaginary part mean.                Coerced to 'double' before local processing.       sigmas = VECTOR, 2-element, any numerical type. Standard                deviations of requested noise distribution.                Coerced to 'double' before local processing.                'sigmas' must be >0d0.       Nrows =  SCALAR, any numerical type. Number of rows                in returned matrix. Coerced to 'integer'                before local processing. Nrows>=1.       Ncols =  SCALAR, any numerical type. Number of columns                in returned matrix. Coerced to INTEGER before                local processing. Mvols>=1    RETURN: MATRIX, type COMPLEX, Nrows X Mcols matrix            of AWGN. ```
Description ``` ```Creates a complex-valued matrix of discrete samples of ADDITIVE WHITE GAUSSIAN NOISE(AWGN) with specified 'means' and standard deviations ('sigmas'). This function returns a Nrows X Ncols COMPLEX matrix where the element real and imaginary parts are samples of the a normally distributed random variable. The means and standard deviations of the real and imaginary parts are indiviually specifiable through column vector arguments 'means' and 'sigmas' as follows: ```    means(1)  = mean of real part    means(2)  = mean of imaginary part    sigmas(1) = standard deviation of real part    sigmas(2) = standard deviation of imaginary part ```
Create a single sample, a vector of 5 samples, and a 5x3 array of complex additive white Gaussian noise. ``` O>cawgn({0,.5},{1,2}) ( 0.40, 0.91)  O>cawgn({0,.5},{1,2},5) { (-0.93, 0.07) (-2.32, 0.25) (-0.62, 0.81) ( 0.23, 0.31) (-0.04, 0.84) } O>cawgn({0,.5},{1,2},5,3) { [ (-0.01, 2.12) , ( 0.44, 1.00) , (-0.75,-1.69) ] [ ( 0.21, 2.60) , ( 0.62,-0.04) , (-0.47, 0.28) ] [ ( 2.01,-1.37) , (-0.18, 0.31) , ( 0.34, 2.52) ] [ (-0.12,-1.61) , (-0.05, 2.65) , (-0.98, 0.27) ] [ ( 0.13, 4.41) , (-0.53,-1.01) , ( 1.31, 1.14) ] } ```