Partial Sample Transmission and Deep Neural Decoding for URLLC V2X System

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 key role in the future ITS. In order to support these emerging services, low-latency and high reliability transmission should be ensured. In the 4G Long Term Evolution (LTE) and 5G New Radio (NR) vehicle-to-everything (V2X) systems, it is very difficult to meet the latency requirement since group of orthogonal frequency division multiplexing (OFDM) symbols are processed in a form of the resource block. In this paper, we propose a novel low-latency packet transmission scheme for V2X systems, referred to as partial sample transmission (PST). In PST, we map the transmit information into subcarrier positions and then decode it using a small fraction of received samples at the receiver. To perform the efficient decoding, we propose a deep learning (DL)-based PST decoding. From the numerical evaluations on the V2X system, we demonstrate that the proposed PST technique outperforms conventional transmission schemes in terms of the block error rate (BLER) and the signaling latency.