Software Listing of Author : "Choqueuse Vincent"

This algorithms implements a blind channel estimation algorithm for Space-Time Block Coded communications. The algorithm exploits the statistical independence of sources before space-time encoding. The channel matrix is estimated by minimizing a kurtosis-based cost function after space time equalization. The proposed can be employed for the general class of linear STBCs including Spatial Multiplexing, Orthogonal, quasi Orthogonal and Non- Orthogonal STBCs. To see an example, just run the following command: one_shot(512,'Alamouti','1',1,5,'PSK',4,50)

  • Platform: Matlab, Scripts

Let us assume the signal model: Y(k)=HX(k)+B(k) This script provides a method for the blind recognition of the number of sources (the size of X(k)). The noise must be spatially white and the number of receivers is assumed to be stricly greater than the number of sources. See reference [CHE91] Chen. W and Wong.K.M. and Reilly. J.P. "Detection of the number of signals: a predicted eigen-thresholdapproach", IEEE Transactions on Signal Processing, 1991.

  • Platform: Matlab, Scripts

Let us assume the following MIMO model: Y(k)=HX(k)+Y(k) This file can detect the number of sources, i.e the size of the vector X(k) from the received signal Y(k). The noise is assumed spatially white and the number of receivers must be strictly greater than the number of sources. For algorithm details, dee reference [WYC95] H.T. Wu, J.F. Yang, and F.K Chen. Source number estimators using transformed gerschgorin radii. IEEE Transactions on Signal Processing, 43(6):1325-1333, 1995.

  • Platform: Matlab, Scripts

Let us assume the following MIMO model: Y(k)=HX(k)+Y(k) This file can detect the number of sources, i.e the size of the vector X(k) from the received signal Y(k). The noise is assumed spatially white and the number of receivers must be strictly greater than the number of sources. For more details about the algorithm, see references: [Law56] D. N. Lawley. Tests of signicance for the latent roots of covariance and correlation matrices. Biometrika, 43(1/2) :128-136, 1956. [Jam69] A.T James. Tests of equality of latent roots of the covariance matrix. Multivariate Analysis, pages 205217, 1969.

  • Platform: Matlab, Scripts

Let us assume the following MIMO model: Y(k)=HX(k)+Y(k) This file can detect the number of sources, i.e the size of the vector X(k) from the received signal Y(k). The noise is assumed spatially white and the number of receivers must be strictly greater than the number of sources. See Reference [WAX85] Wax, M. and Kailath, T., "Detection of signals by information theoretic criteria", IEEE Transactions on Acoustics, Speech and Signal Processing, 1985.

  • Platform: Matlab, Scripts

The zip file contains several matlab files. These files implement the optimal vlassifier (optimal_classifier.m), a sub-optimal classifier (SOS_STBC_Classifier.m) which is based on Second Order Statistic and a low-complexity sub optimal code-parameter classifier (CP_Classifier.m). See reference [1] for the theoretical background. Example: To create the fig 3,4 and 5 of the paper, call the routine: Blind_recognition_STBC2_simulation_results(512,4,'PSK',4) Reference: [1] V. Choqueuse, M. Marazin, L. Collin, K. Yao and G. Burel "Blind recognition of STBC: A Likelihood-based Approach", IEEE Transactions on Signal Processing (IF: 2.33), vol. 58(3), pp : 1290-1299, March 2010.

  • Platform: Matlab, Scripts

This file implements the Multi User Kurtosis Algorithm for Blind Source Separation (see [1] for more details about the method). The zip file contains: - the one_shot.m script file - the MUK_algorithm.m function The one_shot.m file illustrate the bahaviour of the algorithm for a simple MIMO communication using QPSK modulation. Just unzip the zip files in your computer and run the one_shot script. [1] Papadias CB "Globally Convergent Blind Source Separation Based on a Multiuser Kurtosis Maximization Criterion", IEEE Transactions on Signal Processing, 48(12), pp 3508-3519 2000.

  • Platform: Matlab, Scripts

The Central Limit Theorem (CLT) states that the sample average of N i.i.d. random variables approaches the normal distribution. This script displays the probability density function of the sample average of N i.i.d variables with respect to N. The variables can be distributed according to a chi-2, exponential or uniform distribution.

  • Platform: Matlab, Scripts

Space time coding takes profit of the channel diversity to improve the reliability of a communication system. This file contains some of the most famous space time block codes. References: [1] S.M Alamouti " A simple transmitter diversity scheme for wireless communications" IEEE J.Select Areas Communication vol 16,October 1998 [2] V.Tarokh "Space Time BLock Codes from Orthogonal Designs" IEEE Transactions on Information theory, vol 45, July 1999 [3] G.Ganesan and P. Stoica "Space time Block Codes: A Maximum SNR approach", IEEE Transactions on Information Theory, vol 47, may 2001. [4] I. Jafarkhani: "A Quasi Orthogonal Space Time Block Code" IEEE Transactions Letters on Communications, Vol. 49, No.1, 2001

  • Platform: Matlab, Scripts

The sound denoising algorithm is based on the popular spectral subtraction technique. Based on the spectrum of the vuvuzela sound, this denoising technique simply computes an antenuation map in the time-frequency domain. Then, the audio signal is restored by computing the inverse STFT. See [1-3] for more detail about the algorithm. The zip file contains: - the vuvuzela_denoising.m file - the vuvuzela.wav audio file To hear the result of this algorithm, go directly to: http://soundcloud.com/choc29/vuvuzela-correction-with-matlab Note that better denoising audio results could be obtained by properly tuning the algorithm parameters. References: [1] Steven F. Boll, "Suppression of Acoustic Noise in Speech Using Spectral Subtraction", IEEE Transactions on Signal Processing, 27(2),pp 113-120, 1979 [2] Y. Ephraim and D. Malah, 'Speech...

  • Platform: Matlab, Scripts
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