Recurrent neural network based turbo decoding algorithms for different code rates

Abstract

Application of deep learning to error control coding is gaining special attention and neural network architectures on decoding are approached to compare with conventional ones. Turbo codes conventionally use BCJR algorithm for decoding. In this paper, performances of neural Turbo decoder and deep learning-based Turbo decoder are examined. A category of sequential codes are utilized to construct the RSC (Recursive Systematic Convolutional) codes as basic elements for Turbo encoder. Sequential codes suit the requirement of memory element present in convolution codes, which act as components for Turbo encoder. Turbo decoders are constructed by two means; as neural Turbo decoder and deep learning Turbo decoder. Both structures are based on recurrent neural network (RNN) architectures. RNN architectures are preferred due to the presence of memory as a feature. BER performance of both is compared with that of a convolutional Viterbi decoder in awgn channel. Both the structures are studied for different input data-lengths and code rates.

Keywords

• Block error rate
• Signal to noise ratio
• Additive white Gaussian noise
• Recurrent neural network
• Deep learning
• Turbo code