Orthogonal Time Frequency Space and Predictive Beamforming-Enabled URLLC in Vehicular Networks
Weijie Yuan, Jiaqi Zou, Yuanhao Cui, Xinyu Li, Junsheng Mu, Kaifeng Han
Abstract
To fulfill the requirements of future intelligent transportation system in 6G era, ultra-reliable and low-latency vehicular communications is of great importance. In high-mobility scenarios, the conventional orthogonal frequency division multiplexing (OFDM) modulation may fail to work due to high Doppler spreads. Moreover, the dynamic network topology imposes challenges on aligning the beams in multiple antenna systems. In this context, this article introduces a new orthogonal time frequency space (OTFS) and sensing-assisted predictive beamforming framework for supporting reliable and low-latency vehicular communications. In particular, we will first overview the OTFS modulation scheme, which performs data transmission in the delay-Doppler (DD) domain and discuss its superiority in improving communication reliability in vehicular networks. Then the sensing-assisted predictive beamforming scheme will be introduced, which does not rely on dedicated pilots for beam pairing, leading to very low overhead and latency. Benefiting from the DD channel representation, we briefly discuss the potential of channel prediction. Lastly, the challenges and future research directions are summarized.