Non-Parametric Impulsive Noise Mitigation in OFDM Systems

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