Parameter Estimation for Adaptive Impulsive Noise Suppression: A Deep Learning-Based Memoryless Nonlinearity Approach

In the OFDM-based digital terrestrial broadcasting systems, impulsive noise is a significant factor affecting communication quality. A prominent method to suppress impulsive noise is to incorporate a memoryless nonlinearity at the receiver front-end of the OFDM demodulator, in which parameter estimation of memoryless nonlinearity directly impact the effectiveness of impulsive noise suppression. In this paper, we proposes a deep learning-based memoryless nonlinearity approach for impulsive noise suppression. The proposed method can adaptively estimate the parameters of the memoryless nonlinearity in dynamic impulsive noise environments and achieve totically-optimal parameter estimation. To specific, we design a High-Amplitude Priority Downsampling method to extract the key amplitude characteristics from the input signal, which effectively resolves the issue of extracting amplitude features of impulsive noise. Besides, to address the issue of performance degradation due to insufficient training samples, we propose a novel training method that integrates progressive fine-tuning to complete the training only using few samples. Furthermore, we conduct experiments on signal-to-noise ratio (SNR) and bit error rate (BER) of the signal after impulsive noise suppression. The results validate that the parameters estimated by the proposed method can approximate the theoretical optimal values and the proposed method can effectively suppress impulsive noise and outperform the traditional methods in terms of SNR and BER.