A Channel-Blind Decoding for LDPC Based on Deep Learning and Dictionary Learning

Low-density parity-check (LDPC) codes are used to correct encoding errors that occur during transmission, which enjoys an excellent performance. The performance of existing Min-Sum decoders for LDPC codes relies heavily on accurate channel estimation. A two-dimensional blind channel decoding algorithm that does not require precise channel estimation is presented in this paper. The algorithm converts the original one-dimensional signal into a two-dimensional LDPC signal according to the template. Dictionary learning is introduced for pre-filtering, and deep learning is adopted for further denoising and decoding. It is revealed that the two-dimensional blind decoding algorithm has a significant improvement over the traditional belief propagation (BP) decoding algorithm when the channel noise is unknown. Moreover, the combination of dictionary learning and deep learning has a great improvement in performance and data size reduction.