Partial Sample Transmission and Deep Neural Decoding for URLLC-based V2X Systems

Abstract

With the rapid development of intelligent transportation systems (ITS), mission-critical vehicular services such as vehicle platooning, safety alarming, and remote driving play a vital role in the blueprint of the future ITS. In order to support these services, the high reliability and low latency are of great importance. In the 4G LTE/5G NR, it is difficult to satisfy these requirements since multiple OFDM symbols are processed as a bundle. In this paper, we propose a novel low latency packet transmission scheme, referred to as partial sample transmission (PST). Key idea of the proposed scheme is to transform the transmit information into subcarrier positions and then decode it using a small amount of time-domain received samples. In particular, in the PST decoding, we put forth an entirely different approach based on a deep neural network (DNN). From the numerical evaluations on realistic channel models, we demonstrate that the PST scheme outperforms the conventional transmission schemes in terms of the block error rate (BLER) and transmission latency.