Gaussian Mixture Learning for LDPC Coded BICM Receivers With Blanking Nonlinearity

Abstract

With the aid of blanking nonlinearity, the low density parity check (LDPC) coded bitinterleaved coded modulation (BICM) has been jointly considered as a robust mitigation for the impulsive interference in orthogonal frequency division multiplexing (OFDM) systems. However, the Gaussian assumption for the nonlinear channel conditional probability induces the mismatched L-values in the conventional MAP demodulator. In this paper, combined with the pulse blanking optimization via the PEXIT analysis, we propose a novel MAP demodulator based on the Gaussian mixture model (GMM) and estimate the parameters with the expectation-maximization (EM) algorithm. Taking the L-band Digital Aeronautical Communication System Type1 (L-DACS1) as an example, the GMM-based MAP demodulator can obtain the PEXIT thresholds that match the decoding curves well and provide the better BER performance in the interference-limit channel environment.