Proc. IEEE Global Communications Conference, Dec. 5-9, 2011, Houston, TX USA.

Non-Parametric Impulsive Noise Mitigation in OFDM Systems Using Sparse Bayesian Learning

Jing Lin, Marcel Nassar and Brian L. Evans

Department of Electrical and Computer Engineering, Engineering Science Building, The University of Texas at Austin, Austin, TX 78712 USA - -

Draft of Paper - Slides in PowerPoint - Slides in PDF

Standalone Matlab code - LabVIEW demonstration

Note: The above Matlab code is for the sparse Bayesian learning (SBL) algorithm for interference mitigation that uses the interference observed in the null tones in received complex-valued OFDM signals. The Globecom paper figures were generated using real-valued OFDM signals. Also, the above Matlab code doesn't implement the second SBL algorithm in the Globecom paper using data in all tones.

Interference Modeling and Mitigation Toolbox

Smart Grid Communications Mitigation Research at UT Austin


Additive asynchronous impulsive noise limits communication performance in certain OFDM systems, such as powerline communications, cellular LTE and 802.11n systems. Under additive impulsive noise, the fast Fourier transform (FFT) in the OFDM receiver introduces time-dependence in the subcarrier noise statistics. As a result, complexity of optimal detection becomes exponential in the number of subcarriers. Many previous approaches assume a statistical model of the impulsive noise and use parametric methods in the receiver to mitigate impulsive noise. Parametric methods degrade with increasing model mismatch, and require training and parameter estimation. In this paper, we apply sparse Bayesian learning techniques to estimate and mitigate impulsive noise in OFDM systems without the need for training. We propose two nonparametric iterative algorithms:
  1. estimate impulsive noise by its projection onto null and pilot tones so that the OFDM symbol is recovered by subtracting out the impulsive noise estimate; and
  2. jointly estimate the OFDM symbol and impulsive noise utilizing information on all tones.
In our simulations, the estimators achieve 5dB and 10dB SNR gains in communication performance respectively, as compared to conventional OFDM receivers.


  1. "The time-correlation properties of the impulsive noise are not very clear to us. The description and the equations in Section II only seem to give a pdf, presumably of the instantaneous noise sample. How is the process itself described?"

    Answer: In this paper, we assume that the impulsive noise samples are i.i.d. It'll be interesting to see how our methods work in case that the noise is time-correlated. We are working towards deriving a hidden Markov chain model to reflect this correlation.

  2. "Could you explain the statement: 'Although the real and imaginary parts of g are not exactly i.i.d, we approximate them as being such'. I thought they were i.i.d Gaussian.

    Answer: Vector g is the DFT of a real Gaussian vector. The real and imaginary parts of it are not i.i.d. For example, the imaginary part of the first entry is always 0.

  3. "If you don't have space [page limit] constraints, it would be good to explain equations 14-17. Specifically, it is surprising to see that none of those equations had a step where you made a slicing decision on the data."

    Answer: We don't slice the data because the Expectation-Maximization algorithm only works for continuous variables. If some of the variables are discrete, then there's no guarantee on the convergence. The data will be sliced after all the iterations, and the symbol error rate is computed based on the sliced data.

  4. "The results of Section VIII are interesting. We are guessing it does not have any FEC [forward error correction], since this is not mentioned. It may be interesting to see what happens when even a relatively simple FEC scheme like convolutional coding is used."

    Answer: See answer to #5.

  5. "Would it be possible to also try clipping at some level, say 18 dB above the signal rms / median value. This should cover for the PAPR of OFDM itself."

    Answer: You're right. Adding forward error correction and clipping will make our experimental results stronger. Actually, we've seen sparse Bayesian learning applied in peak-to-average power ratio (PAPR) reduction. My initial guess is that the clipping errors could be automatically corrected by our algorithms, since it's relatively sparse.

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Last Updated 02/03/11.