ATRNN: Using Seq2Seq Approach for Decoding Polar Codes

Abstract

Polar codes have been chosen by 3GPP as the official error-correcting codes in the control plane of 5G NR enhanced Mobile Broadband (eMBB) due to their low complexity decoding and near-DMC capacity achievement. Recently, deep learning methods have produced promising results in many fields, including linear and polar code decoding. Using end-to-end DL approach provides flexibility, thus empowering us to integrate powerful DL algorithms into channel decoders. In this paper, we propose a novel one-shot decoding framework for polar codes using Attention-based Recurrent Neural Network (ATRNN). Furthermore, we evaluate the decoding performance of ATRNN using bit error rate (BER) and compare it with the state-of-the-art techniques such as successive cancellation (SC).