UT Austin Radio Frequency Interference (RFI) Modeling and Mitigation Toolbox

Kapil Gulati, Marcel Nassar, Aditya Chopra, Nnaemeka Ben Okafor, Marcus DeYoung, Navid Aghasadeghi, Arvind Sujeeth, and Brian L. Evans
Embedded Signal Processing Laboratory
Department of Electrical and Computer Engineering
The University of Texas at Austin, Austin, TX 78712-1084

02/06/10

RFI Mitigation Research at UT Austin

Introduction

The RFI Mitigation toolbox for MATLAB is designed to provide a simulation environment for generating impulsive noise and quantifying the performance of various parameter estimation algorithms and impulsive noise mitigation algorithms. The toolbox implements generators, parameter estimators, and filters for impulsive noise modeled by Symmetric Alpha Stable (SAS) and Middleton Class A distributions. Bivariate Middleton Class A distributions are also supported. We have used the toolbox to fit measured RFI data.

For communication systems, the toolbox also provides Bayesian detectors for single-antenna receivers for communication signals in additive SAS noise or additive Middleton Class A noise. Two-receiver systems for Middleton Class A noise are also supported. As an alternate to the Bayesian detectors, the impulsive noise filters in the toolbox can be used as a pre-processing step to a standard correlation receiver.

The current version (version 1.4) 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 of the toolbox implements a PAM communication system with correlation detection, Wiener filtering followed by correlation detection, optimal Bayesian detection developed by Spaulding and Middleton [1], and the small-signal approximation of the optimal Bayes Detection [7]. In addition, 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]. The current version also implements the following parameter estimation algorithms: Method of Moments [3], Zabin and Poor [4], and Tsihrintzis [2]. This toolbox also includes various demos that illustrate the usage of the implemented functions, and generate various results. Version 1.3 adds a new demo for statistical modeling of RFI [6][9] which can also be used as a tool for statistical modeling of measured interference datasets.

Downloads

Installation

RFI Mitigation toolbox does not contain a standalone installer. To install it, copy the rfitoolbox directory to your toolbox directory in the MATLAB folder. For example, assuming that MATLAB is installed in C:\Program Files\MATLAB, then a possible destination directory could be C:\Program Files\MATLAB\toolbox. After moving the rfitoolbox directory to the destination directory, the following command should be executed to add the RFI toolbox to your MATLAB path:

addpath(genpath('C:\Program Files\MATLAB\R2007a\toolbox\rfitoolbox\'));

Here, please replace 'C:\Program Files\MATLAB\R2007a\toolbox\' with the destination directory to where you had copied the rfitoolbox folder.

Note: Starting with version 1.3, a main GUI demo has been available to run all other demos included in the release. The main demo can be started by typing 'RFI_StartDemos' on the MATLAB command prompt after completing the aforementioned installation procedure.

Theory and Background Information

The theory and background information are given in an online report and presentation that can be found at the following links: Also, please see [8].

Bug Reports and Feedback

For bugs and feedback, please send e-mail to Marcel Nassar.

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 Transactions 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 environments," IEEE Transactions on Signal Processing, vol. 49, no. 2, pp. 438-441, Feb. 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.
  8. M. Nassar, K. Gulati, M. R. DeYoung, B. L. Evans and K. R. Tinsley, "Mitigating Near-Field Interference in Laptop Embedded Wireless Transceivers", Journal of Signal Processing Systems, Mar. 2009, invited paper.
  9. K. Gulati, A. Chopra, B. L. Evans, and K. R. Tinsley, "Statistical Modeling of Co-Channel Interference", Proc. IEEE Int. Global Communications Conf., Nov. 30-Dec. 4, 2009, Honolulu, Hawaii, accepted for publication.

Mail comments about this page to bevans@ece.utexas.edu.