Hybrid ARQ for URLLC Using Deep Learning

This study proposes early HARQ based on Deep learning techniques to predict the decodability of a received codeword early within few decoding iterations of the initial redundancy version(RV) of the codeword. The transmitter reacts with the transmission of the required RV versions in accordance with the feedback. System level simulation is performed for the performance analysis. The simulation shows the significant improvement of link throughput and reduction of delay in comparison to the traditional HARQ maintaining the BLER performance with traditional HARQ.