Impulsive Noise Recovery and Elimination: A Sparse Machine Learning Based Approach - Liu, S. (IEEE Xplore, 2020)
The performance of orthogonal frequency division multiplexing (OFDM) based wireless vehicular communication systems is faced with the great challenge of impulsive noise (IN), which could limit the application of OFDM in ultra-reliable lowlatency communication scenarios. In this paper, the challenge of IN elimination for OFDM-based wireless systems is efﬁciently overcome by the proposed sparse learning algorithms and probabilistic framework inspired by the emerging machine learning theories. For the ﬁrst time, the sparse machine learning theory is introduced to IN recovery and elimination. Exploiting the measurement vector of IN observed from the reserved null sub-carriers as the input, a novel sparse machine learning based algorithm of sparse cross-entropy minimization is proposed, in which the probability distribution of the IN support is iteratively updated by minimizing the loss function, i.e. the cross-entropy. The proposed algorithm is able to effectively and efﬁciently learn the sparse pattern and converge to the accurate distribution of IN support. To facilitate an accelerated and even more efﬁcient learning process, regularization is imposed on the loss function by adding a weighting parameter in favor of the accurate distribution. The computer simulation results conﬁrm that the proposed scheme outperforms conventional methods while utilizing fewer spectrum resources over wireless vehicular channels.