UT Austin Interference 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, Texas USA

01/22/13

Interference Modeling and Mitigation Research at UT Austin

Translations:
German, courtesy of Katia Osipova.
Polish, courtesy of Alexey Gnatuk.
Romanian, courtesy of azoft.
Russian, courtesy of Oleg Meister.
Slovak, courtesy of Sciologness Team

Introduction

This free toolbox provides MATLAB functions and demonstrations for statistical modeling and mitigation of certain kinds of noise and interference in acoustic systems, power lines, wireless communications and wireless sensor networks [1-10]. The noise and interference can come from other sources in the same frequency band of operation or in adjacent frequency bands.

The toolbox enables a user to

  1. generate impulsive noise/interference
  2. fit measured data to impulsive noise models
  3. apply nonlinear filters to denoise a signal in impulsive noise
  4. improve detection performance of a signal in impulsive noise
Engineers and scientists are using the toolbox in astronomy, communication systems and analog/RF circuit design.

In communication systems, we model asynchronous interference as additive impulsive noise. We've derived the impulsive noise distributions by using statistical-physical models of propagation:

Femtocells would fit a Gaussian mixture model if only out-of-cell interference is considered. If both in-cell and out-of-cell interference is considered, then the symmetric alpha stable distribution would apply.

Impulsive noise is also a problem in wireline communications. In powerline communication systems, for example, impulsive noise is the dominant noise component. The impulsive noise arises from many sources such as switching circuitry and external transmissions. We have shown that the impulsive noise distribution follows a Gaussian mixture model. In certain cases, the Gaussian mixture model simplifies to a Middleton Class A model.

When communication receivers are designed assuming that the only additive noise source is Gaussian spectrally-flat noise, the presence of additive impulsive noise can cause severe degradation in timing recovery, frame synchronization, equalization, detection and error correction subsystems. Conversely, by redesigning the receiver with additive impulsive and thermal noise in mind, the receiver can achieve up to 20 dB of SNR gain in the presence of impulsive noise. We have achieved this improvement by adding a pre-filter, or changing the detector, or adapting a Turbo decoder. An SNR gain of 20 dB would translate into a significant reduction in bit error rate (by 1-2 orders of magnitude) [10] or improvement in bit rate (of up to 3-6 bits/s/Hz) in interference-limited channels. We have used RF statistics to derive the tradeoffs in throughput, delay and reliability and demonstrate the throughput can be doubled and reliability improved over an existing medium access layer protocol [11].

Features

Our interference modeling and mitigation toolbox enables the generation and parameter estimation [2-4] of time series for the following impulsive distributions:

The Middleton Class A model is a special case of Gaussian mixture.

We have used the toolbox to fit radio frequency interference data measured at baseband [6][7][8][9].

The toolbox also generates the following time series:

The toolbox implements several impulsive noise reduction filters:

In communication systems, the toolbox can be also used to design discrete-time signal processing algorithms at baseband for interference-aware transceivers by using easy-to-use GUI tools built on top of interference modeling and mitigation algorithms.

The toolbox supports single-transmitter single-receiver (1x1) systems, as well as two-transmitter, two-receiver (2x2) systems. For 1x1 communication systems, the toolbox uses pulse amplitude modulation (PAM). Receiver choices are correlation detection, Wiener filtering followed by correlation detection, optimal Bayes detection [1], and the small-signal approximation of optimal Bayes detection [8]. For 2x2 communication systems, the toolbox uses quadrature amplitude modulation (QAM). Transmission uses either spatial multiplexing or Alamouti coding. Reception choices include Gaussian maximum likelihood (ML), zero forcing, Middleton Class A ML, and suboptimal Middleton Class A ML receivers. [6]

Most Recent Toolbox Version for Download

Installation

The Interference Modeling and 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 interference modeling and mitigation 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.

Older Toolbox Versions Available for Download

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.
  10. K. Gulati, B. L. Evans, J. G. Andrews and K. R. Tinsley, "Statistics of Co-Channel Interference in a Field of Poisson and Poisson-Poisson Clustered Interferers", IEEE Transactions on Signal Processing, vol. 58, no. 12, Dec. 2010.
  11. K. Gulati, R. K. Ganti, J. G. Andrews, B. L. Evans and S. Srikanteswara, "Local Delay and Throughput-Delay-Reliability Tradeoff in Wireless Ad Hoc Networks with Temporally Dependent Interference", IEEE Transactions on Signal Processing, to be submitted.


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