Deep Unfolding-Aided Sum-Product Algorithm for Error Correction of CRC Coded Short Message

Abstract

This paper proposes a deep unfolding-aided sum-product algorithm (SPA) for error correction decoding of cyclic redundancy check (CRC) coded short message. SPA is a practical decoding algorithm for linear codes without requiring enormous computational complexity. However, if the SPA is used as it is for CRC codes, belief correlation and outliers will be induced in the iterative decoding process, resulting in lousy correction capability. To compensate for this drawback, we design a SPA-based decoding process for CRC code that incorporates a data-driven design based on deep learning and learning optimization of in-ternal trainable parameters. Considering the operation principle of soft-decision decoder, a novel loss function based on a weighted average of negentropy, which is a key measure to evaluate the Gaussianity, and BCE of the decoder output is proposed. Numerical results show that the proposed algorithm improves the bit error rate (BER) performance with deep unfolding and negentropy-aware loss function.