Software Listing of Author : "G. Levin"
- Alamouti Scheme with GUI
- License: Freeware
- Price: 0.00


ALAMOUTI performs Monte-Carlo simulation and estimates Bit Error Rate (BER) of Alamouti Scheme [1] over Rayleigh channel. The scheme presumes 2 transmit (Tx) and arbitrary number of receive (Rx) elements. If Rx=1 (one receive element) the ALAMOUTI is transformed to the order 2 transmit diversity scheme with Maximum Ratio Combining (MRC). The modulation format is MPSK with arbitrary order M which can be controlled by user. The simulation parameters are entered using the built graphic user interface (GUI) and contains: 1. Number of pair symbols from both Tx elements to be transmitted: N. This number should be at least 10 times more that the expected 1/BER to provide low estimation error. 2. MPSK order M, must be a power of 2. 3. Signal-to-Noise-Ratio (SNR) in dB as a ratio of the average received power at one Rx element over the...
- Platform: Matlab, Scripts
- Circular Cross Correlation
- License: Shareware
- Price:


CXCORR Circular Cross Correlation function estimates. CXCORR(a,b), where a and b represent samples taken over time interval T which is assumed to be a common period of two corresponding periodic signals. a and b are supposed to be length M row vectors, either real or complex. [x,c]=CXCORR(a,b) returns the length M-1 circular cross correlation sequence c with corresponding lags x. The circular cross correlation is: c(k) = sum[a(n)*conj(b(n+k))]/[norm(a)*norm(b)]; where vector b is shifted CIRCULARLY by k samples. The function doesn't check the format of input vectors a and b! For circular covariance between a and b look for CXCOV(a,b) in http://www.mathworks.com/matlabcentral/fil...bjectId=1093734 Reference: A. V. Oppenheim, R. W. Schafer and J. R. Buck, Discrete-Time Signal Processing, Upper Saddler River, NJ : Prentice Hall,...
- Platform: Matlab, Scripts
- Circular Cross Covariance
- License: Shareware
- Price:


CXCOV Circular Cross Covariance function estimates. CXCOV(a,b), where a and b represent samples taken over time interval T, which is assumed to be a common period of two corresponding periodic signals. a and b are supposed to be length M row vectors, either real or complex. [x,c]=CXCOV(a,b) returns the length M-1 circular cross covariance sequence c with corresponding lags x. The circular cross covariance is the normalized circular cross correlation function of two vectors with their means removed: c(k) = sum[a(n)-mean(a))*conj(b(n+k)-mean(b))]/[norm(a-mean(a))*norm(b-mean(b))]; where vector b is shifted CIRCULARLY by k samples. The function doesn't check the format of input vectors a and b! For circular correlation between a and b look for CXCORR(a,b) in http://www.mathworks.com/matlabcentral/fil...bjectId=1093734 Reference:...
- Platform: Matlab, Scripts
- Geary Test
- License: Shareware
- Price:


GTEST: Single sample Geary goodness-of-fit hypothesis test. H=GTEST(X,ALPHA) performs the Geary test to determine whether the null hypothesis of composite normality PDF is a reasonable assumption regarding the population distribution of a random sample X with the desired significance level ALPHA. H indicates the result of the hypothesis test according to the MATLAB rules of conditional statements: H=1 => Do not reject the null hypothesis at significance level ALPHA. H=0 => Reject the null hypothesis at significance level ALPHA. The Geary's hypotheses and test statistic are: Null Hypothesis: X is normal with unknown mean and variance. Alternative Hypothesis: X is not normal. The test evaluates the "good estimator of STD(X) for normal distribution"/"reasonable estimator of STD(X)" ratio. If X taken from a non-normal distribution...
- Platform: Matlab, Scripts
- Pearson Chi Square Hypothesis Test
- License: Shareware
- Price:


CHI2TEST: Single sample Pearson Chi Square goodness-of-fit hypothesis test. H=CHI2TEST(X,ALPHA) performs the particular case of Pearson Chi Square test to determine whether the null hypothesis of composite normality PDF is a reasonable assumption regarding the population distribution of a random sample X with the desired significance level ALPHA. H indicates the result of the hypothesis test according to the MATLAB rules of conditional statements: H=1 => Do not reject the null hypothesis at significance level ALPHA. H=0 => Reject the null hypothesis at significance level ALPHA. The Chi Square hypotheses and test statistic in this particular case are: Null Hypothesis: X is normal with unknown mean and variance. Alternative Hypothesis: X is not normal. The random sample X is shifted by its estimated mean and normalized by its...
- Platform: Matlab, Scripts
- Rayleigh Channel Pearson Chi Square Test
- License: Shareware
- Price:


CHI2RAYLTEST: Single sample Pearson Chi Square goodness-of-fit statistical test to examine a null hypothesis of Rayleigh Channel. H=CHI2RAYLTEST(X,ALPHA) performs the particular case of Pearson Chi Square test to determine whether the null hypothesis of a Rayleigh channel realization is a reasonable assumption regarding the population distribution of a complex random sample X with the desired significance level ALPHA. H indicates the result of the hypothesis test according to the MATLAB rules of conditional statements: H=1 => Do not reject the null hypothesis at significance level ALPHA. H=0 => Reject the null hypothesis at significance level ALPHA. The Chi Square hypotheses and test statistic in this particular case are: Null Hypothesis: X is a base-band Rayleigh channel realization with unknown mean and variance. Alternative...
- Platform: Matlab, Scripts
- Smirnov Cramer Von Mises Test
- License: Shareware
- Price:


Single sample Smirnov-Cramer-Von Mises goodness-of-fit hypothesis test. H = MTEST(X,ALPHA) performs the particular case of Smirnov-Cramer-Von Mises test to determine whether the null hypothesis of composite normality CDF is a reasonable assumption regarding the population distribution of a random sample X with the desired significance level ALPHA. The Smirnov-Cramer-Von Mises test is based on interpolation procedure, so the significance level is restricted to 0.001 <= ALPHA <= 0.10. H indicates the result of the hypothesis test according to the MATLAB rules of conditional statements: H=1 => Do not reject the null hypothesis at significance level ALPHA. H=0 => Reject the null hypothesis at significance level ALPHA. Let S(x) be the empirical c.d.f. estimated from the sample vector X, F(x) be the corresponding true normal...
- Platform: Matlab, Scripts
