~6dB

~8dB

~4dB

~10dB

~6dB

Non-Parametric Impulsive Noise Mitigation in OFDM SystemsJing Lin, Marcel Nassar, and Brian L. Evans, The University of Texas at Austin

Project supported by National Instruments, and by SRC GRC under Task Id 1836.063.

Simulated Performance

Objective: Estimate and mitigate impulsive noise without any assumption of noise statistics and without any training overhead

OFDM

DFT sub-matrix

Impulsive noise AWGN

Non-Parametric Mitigation

Noise estimation by observing null tones • No data carried by null tones• Underdetermined linear regression• Impulsive noise is sparse• Sparse Bayesian learning (SBL) approach Prior:

Likelihood: Posterior:

o Solved by expectation maximization o and converge to a sparse vector due to the

sparsity promoting prior

Noise estimation by observing all tones• Improved accuracy• Assuming known channel• Iteratively updating noise and data estimates

Gaussian Mixture Model Middleton Class A Model

Communication performance (Symbol error rate (SER) ) in different impulsive noise scenarios

Impulsive Noise at Wireless Receivers

Antenna

Baseband processor

Computational Platform• Clocks, busses, processors• Co-located transceivers

Wireless CommunicationSources

Uncoordinated Transmissions

Non-CommunicationSources

Electromagnetic radiations

Sources of impulsive noise

Throughput performance (Wifi, channel 7) [J. Shi et al., 2006]

• OFDM transmits data over multiple independent subcarriers (tones)

• FFT spreads out impulsive noise across all subcarriers

Parametric Methods Non-Parametric MethodsAssumption on noise statistics Yes No

Training needed Yes NoSuffer from model mismatch Yes No

Computational Complexity: O(N2M) (using M null tones), O(N3) (using all N tones)

• MMSE w/ (w/o) CSI: a parametric minimum mean square error estimator w/ (w/o) channel state information• CS+LS: a compressive sensing and least squares based non-parametric approach

• Two non-parametric methods for impulsive noise mitigation in OFDM systems

• 5-10dB signal-to-noise ratio (SNR) gains in simulation • Algorithms being implemented in LabVIEW FPGA

Conclusion