Clipping Noise Estimation Based on Deep Complex Neural Network with Sparsity Constraint

Abstract

Clipping noise estimation and cancellation are essential in orthogonal frequency division multiplexing (OFDM) systems when clipping is performed to reduce the peak-to-average power ratio (PAPR). Motivated by the richer representational capacity of complex numbers and the fact that communication is a complex-valued problem, a novel clipping noise estimation scheme based on deep complex neural network is proposed in this paper. Specifically, the clipping noise is determined by a deep complex network, namely clipping noise estimation network (CNE-Net), such that the mean square error (MSE) and the sparsity of the estimated clipping noise are jointly optimized. Besides, an ordering based zero-forcing scheme is utilized to further ensure the sparsity of the estimated clipping noise. Simulation results show that the proposed CNE-Net shows comparable performance with the conventional decision-aided reconstruction (DAR) scheme and can achieve better performance than the one-iteration DAR scheme when the clipping noise is not sparse enough. In summary, the CNE-Net has a good capability to estimate the clipping noise from noise-affected features.