Intel Seminar

Radio Frequency Interference Sensing and Mitigation in Wireless Receivers

Prof. Brian L. Evans
Dept. of Electrical and Computer Engineering
Wireless Networking and Communications Group
The University of Texas at Austin, Austin, Texas

Lead graduate students: Mr. Aditya Chopra, Mr. Kapil Gulati and Mr. Marcel Nassar

Current collaboration with Dr. Eddie Xintian Lin, Dr. Alberto Alcocer Ochoa, Dr. Kathyayani Srikanteswara and Mr. Keith R. Tinsley at Intel

Tuesday, April 12, 2010

Slides in PowerPoint 2007 format and PowerPoint 2003 format

Video demonstrations


Wireless transceivers are affected by radio frequency interference (RFI) generated from nearby electronic devices (e.g. microwave ovens), coexisting wireless communication sources, and computational platform clocks/busses. RFI is well modeled using non-Gaussian impulsive statistical distributions and can severely degrade the communication performance of wireless transceivers designed under the assumption of additive Gaussian noise. The problem intensifies with higher reuse of radio spectrum and shrinking form factor of the computational platform.

In the first part of the talk, we present our results on statistical modeling and mitigation of RFI in wireless receivers. In particular, we first establish the applicability of the Symmetric Alpha Stable, Middleton Class A, and Gaussian mixture distributions to model RFI in various interference scenarios. Scenarios include Wi-Fi, Wimax, cellular, ad hoc, and wireless sensor networks. Using these statistical models of RFI, we discuss several filtering and detection methods to mitigate RFI for single- and two-antenna receivers. We demonstrate 1-2 orders of magnitude reduction in bit error rate for the same transmission rate, and evaluate design tradeoffs of our proposed methods. RFI modeling also has the potential to improve communication performance by 1-2 orders of magnitude at the medium access control layer.

In the second part of the talk, we demonstrate our freely distributable RFI modeling and mitigation toolbox. Our toolbox can be used to design RFI immune transceivers using easy-to-use GUI tools built on top of RFI modeling and mitigation algorithms. The toolbox can be used by a system design engineer for platform analysis/design and a communications engineer for wireless network performance analysis. We how to use the toolbox to

Our toolbox is available at

This research has been supported by Intel since January 2007.

(30 min) Presentation
(20 min) RFI Modeling and Mitigation Toolbox Demo


Prof. Brian L. Evans is an IEEE Fellow "for contributions to multicarrier communications and image display". In multicarrier communications, his group developed the first linear complexity algorithm that allocates resources to optimize bit rates in multiuser OFDM systems (for cellular and WiMax) and is realizable in fixed-point hardware/software. His group also developed the first ADSL equalizer training method that maximizes a measure of bit rate and is realizable in real-time fixed-point software. In image display, his group's primary contribution is in the design, analysis, and quality assessment of halftoning by error diffusion for real-time processing by printer pipelines. (Error diffusion is two-dimensional data conversion by sigma-delta modulation.) He has graduated 16 PhD students and published more than 180 refereed conference and journal papers. He received a 1997 National Science Foundation CAREER Award.


Question #1. How do errors in parameter estimates for RFI statistical models affect communication performance of pre-filtering based on those parameter estimates?

Answer #1. The myriad filter for pre-processing has a single parameter whose optimal value is computed as follows:

k(alpha) = sqrt(alpha / (2 - alpha)) y^(1/alpha)

Here, alpha is the exponent in the Symmmetric Alpha Stable distribution where 0 < alpha < 2, and y is the dispersion parameter (analogous to variance).

Here are the values of derivation of k(alpha) with respect to alpha for selected values of alpha:

alpha     k'(alpha)
-----     ---------
0.5       0.7698 sqrt(y) + 0.57735 sqrt(y) ln(y)
1.0       y + y ln(y)
1.5       2.31 y^(3/2) + 1.73205 y^(3/2) ln(y)
For y = 1, k'(alpha) varies from 0.7698 to 2.31 for 0.5 < alpha < 1.5. In fitting RFI data, we have found that alpha > 0.5. Pertubations in k are particularly severe as alpha -> 0 and as alpha -> 2.

Question #2. In your presentation, you showed that the Myriad prefilter can mitigate RFI with 10 samples/symbol. Can the Myriad prefilter still mitigate RFI with fewer samples/symbol?

Answer #2. Yes, Myriad pre-filtering can still improve communication performance. The size of the sliding window over which the Myriad filtering operation is done has to be modified appropriately. The number of samples in the sliding window should generally be smaller than the number of samples per symbol. The performance improvement is larger for a higher number of samples per symbol. In alpha stable noise with alpha=0.8, the symbol error rate decreases by two orders of magnitude for the Myriad filter vs. the matched filter for 4 samples/symbol, 3 samples/window, and 3 dB SNR in simulations.

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