文件列表:

rfitoolbox\RFIGenerators\ClassA\RFI_MakePDFClassA.m (2837, 2014-02-12)
rfitoolbox\RFIGenerators\ClassA\RFI_MakeEnvelopeDataClassA.m (2901, 2014-02-12)
rfitoolbox\RFIGenerators\ClassA\RFI_MakeDataClassA.m (3274, 2014-02-12)
rfitoolbox\RFIGenerators\BiVarClassA\RFI_MakeDataBiVarClassA.m (2446, 2014-02-12)
rfitoolbox\RFIGenerators\AlphaStable\RFI_MakeDataAlphaStable.m (1853, 2014-02-12)
rfitoolbox\RFIDetection\MIMO\Gaussian\RFI_AlamoutiRx.m (1097, 2014-02-12)
rfitoolbox\RFIDetection\MIMO\Gaussian\RFI_TwoAntennaMLRx.m (1893, 2014-02-12)
rfitoolbox\RFIDetection\MIMO\Gaussian\RFI_ZeroForcingRx.m (1076, 2014-02-12)
rfitoolbox\RFIDetection\MIMO\BiVarClassA\RFI_BiVarClassAMLRx.m (3193, 2014-02-12)
rfitoolbox\RFIDetection\MIMO\BiVarClassA\RFI_ApproxFuncPhiSubOpt.m (1677, 2014-02-12)
rfitoolbox\RFIDetection\MIMO\BiVarClassA\RFI_BiVarClassASubOptML4Rx.m (3256, 2014-02-12)
rfitoolbox\RFIDetection\MIMO\BiVarClassA\RFI_BiVarClassASubOptML2Rx.m (3431, 2014-02-12)
rfitoolbox\RFIDetection\SISO\RFI_myriad_apx.m (1257, 2014-02-12)
rfitoolbox\RFIDetection\SISO\RFI_detect_myriad.m (2844, 2014-02-12)
rfitoolbox\RFIDetection\SISO\RFI_detect_opt.m (1972, 2014-02-12)
rfitoolbox\RFIDetection\SISO\RFI_detect_opt_quant.m (1969, 2014-02-12)
rfitoolbox\RFIDetection\SISO\RFI_detect_appx.m (1699, 2014-02-12)
rfitoolbox\RFIDetection\SISO\RFI_bi_detector.m (2406, 2014-02-12)
rfitoolbox\RFIDetection\SISO\RFI_myriad_opt.m (1887, 2014-02-12)
rfitoolbox\RFIDetection\SISO\RFI_bi_detector_quant.m (2589, 2014-02-12)
rfitoolbox\RFIDetection\SISO\RFI_nonlinearity.m (1618, 2014-02-12)
rfitoolbox\RFIDetection\SISO\RFI_detect_cor.m (1127, 2014-02-12)
rfitoolbox\RFIDetection\SISO\RFI_detect_wiener.m (1318, 2014-02-12)
rfitoolbox\RFIDetection\SISO\RFI_bi_detector_appx.m (2081, 2014-02-12)
rfitoolbox\RFIParamEst\ClassA\RFI_EMParamK_AFixed.m (2727, 2014-02-12)
rfitoolbox\RFIParamEst\ClassA\RFI_EMParamA.m (3908, 2014-02-12)
rfitoolbox\RFIParamEst\ClassA\RFI_EMParamK.m (3994, 2014-02-12)
rfitoolbox\RFIParamEst\ClassA\RFI_EstMethodofMoments.m (1650, 2014-02-12)
rfitoolbox\RFIParamEst\ClassA\RFI_EMTwoParamEst.m (7037, 2014-02-12)
rfitoolbox\RFIParamEst\ClassA\RFI_EMCalculateObjFunc.m (2475, 2014-02-12)
rfitoolbox\RFIParamEst\BiVarClassA\RFI_MOMBiVarClassAEst.m (3023, 2014-02-12)
rfitoolbox\RFIParamEst\AlphaStable\RFI_EstAlphaS_Alpha.m (2289, 2014-02-12)
rfitoolbox\RFIParamEst\AlphaStable\RFI_EstAlphaS_Dispersion.m (1767, 2014-02-12)
rfitoolbox\RFIParamEst\AlphaStable\RFI_EstAlphaS_Localization.m (996, 2014-02-12)
rfitoolbox\RFIDemos\neg_logo.jpg (13645, 2014-02-12)
rfitoolbox\RFIDemos\wncg-sm.jpg (7028, 2014-02-12)
rfitoolbox\RFIDemos\ParamEst\rfidemo_pest.m (19954, 2014-02-12)
rfitoolbox\RFIDemos\ParamEst\rfidemo_pest.fig (9545, 2014-02-12)
rfitoolbox\RFIDemos\ESPL_logo.jpg (56761, 2014-02-12)
... ...

README RFI (Radio Frequency Interference) Mitigation MATLAB ToolBox Authors: Marcel Nassar (nassar@ece.utexas.edu) Kapil Gulati (gulati@ece.utexas.edu) DATE: April 2, 2009 Contents _________ * Introduction * Installation * Theory and Background Information * Compatibility * Bug Reports and Feedback * Version Updates and Bug Fixes * References Introduction _____________ The RFI Mitigation toolbox for MATLAB is designed to provide a simulation environment for generating radio frequency interference and quantifying the performance of various parameter estimation algorithms and interference mitigation algorithms. It is composed of various functions used to generate various types of noise statistics and to perform noise cancellation and detection. The current version (ver 1.2.1 ) supports the generation of Middleton Class A, Symmetric Alpha Stable, and the bivariate Middleton Class A random variables. For the evaluation of communication performance under the presence of the aforementioned noise types, the current version (ver 1.2.1 ) of the toolbox implements a PAM communication system with correlation detection, Wiener filtering followed by correlation detection, the optimal Bayes detection developed by Spaulding and Middleton [1], and the small-signal approximation of the optimal Bayes Detection. Further the toolbox implements a 2x2 MIMO communication system using M-QAM modulation, spatial multiplexing and alamouti transmission strategies with optimal Gaussian maximum likelihood (ML) receiver, optimal and suboptimal ML receivers in the presence of bivariate Middleton Class A noise [6]. In addition to that, it implements the following parameter estimation algorithms: Method of Moments [3], Zabin and Poor [4], Tsihrintzis [2]. This toolbox also includes various demos that illustrate the usage of the implemented functions, and generate various results. Ver 1.2.1 also added a demo providing capability to send user files through a impulsive noise channels. Installation _____________ The current version (ver 1.2.1 ) of the RFI Mitigation toolbox does not contain a stand alone installer. To install it, the copy the given the RFI directory to your toolbox directory in the MATLAB folder. For example assume that the MATLAB is installed in C:\Program Files\MATLAB. Then a possible destination directory can be C:\Program Files\MATLAB\toolbox. After that the following command should be executed to add the RFI toolbox to your MATLAB paths: addpath(genpath('C:\Program Files\MATLAB\R2007a\toolbox\rfi')); where you replace 'C:\Program Files\MATLAB\R2007a\toolbox\' with the directory to where you copied the rfi folder. Theory and Background Information __________________________________ The theory and the background information are given in an online report and presentation that can be found at the following links: Report: http://users.ece.utexas.edu/~bevans/projects/rfi/reports/RFIReportSpring2007.doc Presentation: http://users.ece.utexas.edu/~bevans/projects/rfi/talks/Oct2008RFIMitigationSlidesCMU.ppt Compatibility ______________ This toolbox has been tested in Matlab 7.0 and 7.1. The following command-toolbox dependencies exit and are needed for the toolbox to run properly: Command Toolbox -------- -------- * qammod, qamdemod Communication Toolbox * pammod, pamdemod Communication Toolbox * moment Statistics Toolbox * rcosine Communication Toolbox * xcorr Signal Processing Toolbox * random Statistics Toolbox * randsample Statistics Toolbox Version Updates and Bug Fixes ______________________________ Version 1.2.1 ------------- * Fixed a bug in RFI_MakePDFClassA where the input parameter M was getting reassigned. Version 1.2 ------------ * Added a new demo for file transmission across the an impulsive channel with alpha stable noise and class A middleton noise, with some video transmission capability (still in beta). * Added function to support this demo are: RFI_getANDmodFDATA.m and RFI_decANDwrtFDATA.m . Version 1.2 beta ---------------- * Added functions for generation of bivariate Middleton Class A noise. * Modified the Middleton Class A noise generators for improved computational performance. * Added functions to implement 2x2 MIMO receivers in the presence of Gaussian and bivariate Middleton Class A noise. * Added demo for a 2x2 MIMO system in the presence of RFI: RFI_DemoTwoByTwoMIMO * Added small signal approximation, quantized pdf implementation of the bayesian detection in the presence of Middleton Class A noise Version 1.1 beta ---------------- * Added Myriad functions RFI_myriad_apx, RFI_myriad_opt, RFI_myriad_res, RFI_detect_myriad. * Added a demo illustrating the performance of these methods in the presence of alpha stable noise: rfidemo_rx_aplha. * Fixed the fact that S(alpha,disper,delta) = disper ^ (1/alpha) * S(alpha, 1, 0) + delta in RFI_EstAlphaS_Dispersion function. The implemented equation for the dispersion estimator is modified accordingly and is now in-sync with [2]. * Fixed the fact that S(alpha,disper,delta) = disper ^ (1/alpha) * S(alpha, 1, 0) + delta in RFI_MakeDataAlphaStable. Previously, this function was incorrectly implemented using disper * S(alpha,1,0) + delta. Corresponding changes have been made to the estimator for dispersion parameter. * Removed the old implementation of RFI_NoiseSamplesClassA, and made this function just an encapsulation for RFI_makeScalarPDFClassA and RFI_makeScalarNoiseClassA_pdf (written by DeYoung) to improve computational performance. * Implemented a recursive implementation of RFI_ClassApdf that improves perfomance and increases the range of usable As. * Added an alpha stable nois option to the Communication Performance Demo. Bug Reports and Feedback _________________________ For bugs and feedback, email me at nassar@ece.utexas.edu. References ___________ [1] A. Spaulding and D. Middleton, “Optimum reception in an impulsive interference environment-part I: Coherent detection,” IEEE Transactions on Communications, vol. 25, no. 9, pp. 910–923, 1977. [2] G. A. Tsihrintzis and C. L. Nikias, "Fast estimation of the parameters of alpha-stable impulsive interference", IEEE Transactions on Signal Processing, vol. 44, no 6, pp. 1492-1503, June 1996. [3] D. Middleton, “Procedures for determining the properties of the first-order canonical models of Class A and Class B electromagnetic interference”, IEEE Transactions on Electromagnetic Compatibility, vol. 21, pp. 190-208, Aug. 1979. [4] S. M. Zabin and H. V. Poor, “Efficient estimation of Class A noise parameters via the EM [Expectation-Maximization] algorithms”, IEEE Transaction on Information Theory, vol. 37, no. 1, pp. 60-72, Jan. 1991. [5] J.R. Gonzalez and G.R. Arce. "Optimality of the myriad in practical impulsive-noise enviroments," IEEE Trans. on Signal Processing, vol 49,no.2, pp. 438-441, February 2001. [6] K. Gulati, A. Chopra, R. W. Heath, Jr., B. L. Evans, K. R. Tinsley, and X. E. Lin, "MIMO Receiver Design in the Presence of Radio Frequency Interference", Proc. IEEE Int. Global Communications Conf., Nov. 30-Dec. 4th, 2008, New Orleans, LA USA. [7] M. Nassar, K. Gulati, A. K. Sujeeth, N. Aghasadeghi, B. L. Evans and K. R. Tinsley, "Mitigating Near-Field Interference in Laptop Embedded Wireless Transceivers", Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Proc., Mar. 30-Apr. 4, 2008, Las Vegas, NV USA.

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