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