Deep learning for the decoding of low-density parity check codes

Abstract

In digital communication, Linear block codes, especially Low-density parity-check codes, have seen an extensive application nowadays. LDPC codes were used in 5G wireless communication as they proved to be capable of having excellent decoding abilities. Various algorithms were applied to decode LDPC as Belief Propagation, Min-sum and much more. Furthermore, with the rise of artificial intelligence and deep learning applications in today’s technology, many literatures have recently proposed special types of channel code decoders by integrating deep learning to it. So, the target of this research is to implement a deep learning algorithm on the decoding of LDPC codes. Applying deep learning decoders achieves an improvement in the bit and frame error rates performance for LDPC codes and were able to reduce the complexity of the calculations. Finally, throughout the simulation results, the examination of the decoder and its parameters revealed that a deep learning method surpassed the classical technique for decoding LDPC codes. Second, reducing the LDPC code rate will improve decoding performance. Lastly, the use of a single iteration during message update surpassed the use of two previous iterations.