Achieving the stringent requirements of ultra reliable low latency communication (URLLC) in vehicular networks, characterized by high mobility, presents significant challenges. Orthogonal time frequency space (OTFS) modulation has surfaced as a promising solution to tackle Doppler shifts and delay spreads in high-mobility wireless channels by mapping symbols into the delay-Doppler (DD) domain. This paper introduces a novel OTFS-enabled transceiver framework for predictive beamforming in vehicular networks. The framework employs frequency division duplex (FDD) to diminish latency and enhance flexibility, with the predictive beamforming technique implemented at the transmitter to improve the received signal strength (RSS). Owing to the latency constraints of URLLC, predictive beamforming necessitates leveraging historical channel state information (CSI) in the DD domain. In this context, a deep learning (DL) approach using the ConvLSTM network is harnessed for predictive beamforming, concentrating on capturing spatial-temporal correlations of high-mobility wireless channels from historical DD domain CSI. Extensive simulations validate the efficacy of our proposed DL-based predictive beamforming for OTFS-enabled URLLC in vehicular networks.

In satellite-terrestrial links, the high-speed movement of low earth orbit (LEO) satellites induces significant doppler effects. By employing orthogonal time frequency space (OTFS) modulation technology, information symbols are effectively mapped into the delay-doppler domain, significantly mitigating the adverse impacts of doppler effects on transmissions. Furthermore, the short packet is introduced into satelliteterrestrial communications to meet diverse latency requirements. Due to the limited channel coding capacity of short packets, adjusting the channel coding rate and symbol modulation order according to the channel conditions is crucial in satellite communications. However, real-time channel state information feedback is precluded by the rapid variations in satellite-terrestrial channels, the large feedback latency, and the limited resources for frequent feedback. Therefore, implementing a feedback-free transmission approach is particularly essential. In this paper, we design a feedback-free OTFS-based adaptive modulation and coding (AMC) scheme to accommodate varying communication requirements and conditions. Extensive simulations with different orbital altitudes, carrier frequencies, and packet lengths have shown that the proposed approach can be effectively adapted to LEO satellite channels, enabling a feedback-free AMC in the LEO satellite-terrestrial communications.