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Non-Parametric Impulsive Noise Mitigation in OFDM Systems

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

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Non-Parametric Impulsive Noise Mitigation in OFDM Systems

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  1. Jing 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. Objective: Estimate and mitigate impulsive noise without any assumption of noise statistics and without any training overhead Impulsive Noise at Wireless Receivers OFDM Throughput performance (Wifi, channel 7) [J. Shi et al., 2006] • OFDM transmits data over multiple independent subcarriers (tones) Sources of impulsive noise • Computational Platform • Clocks, busses, processors • Co-located transceivers ~10dB Antenna ~6dB ~6dB ~8dB Non-Parametric Impulsive Noise Mitigation in OFDM Systems Non-Communication Sources Electromagnetic radiations Wireless Communication Sources Uncoordinated Transmissions • FFT spreads out impulsive noise across all subcarriers Baseband processor ~4dB Simulated Performance Non-Parametric Mitigation Communication performance (Symbol error rate (SER) ) in different impulsive noise scenarios • Noise estimation by observing null tones • No data carried by null tones • Underdetermined linear regression • Impulsive noise is sparse • Sparse Bayesian learning (SBL) approach DFT sub-matrix Prior: Likelihood: Posterior: Impulsive noise AWGN • Solved by expectation maximization • 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 • 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 Computational Complexity: O(N2M) (using M null tones), O(N3) (using all N tones) Conclusion • 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 LabVIEWFPGA

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