Integrated Sensing and Communication-assisted Orthogonal Time Frequency Space Transmission for Vehicular Networks
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Abstract (arXiv)
Orthogonal time frequency space (OTFS) modulation is a promising candidate for supporting reliable information transmission in high-mobility vehicular networks. In this paper, we consider the employment of the integrated (radar) sensing and communication (ISAC) technique for assisting OTFS transmission in both uplink and downlink vehicular communication systems. Benefiting from the OTFS-ISAC signals, the roadside unit (RSU) is capable of simultaneously transmitting downlink information to the vehicles and estimating the sensing parameters of vehicles, e.g., locations and speeds, based on the reflected echoes. Then, relying on the estimated kinematic parameters of vehicles, the RSU can construct the topology of the vehicular network that enables the prediction of the vehicle states in the following time instant. Consequently, the RSU can effectively formulate the transmit downlink beamformers according to the predicted parameters to counteract the channel adversity such that the vehicles can directly detect the information without the need of performing channel estimation. As for the uplink transmission, the RSU can infer the delays and Dopplers associated with different channel paths based on the aforementioned dynamic topology of the vehicular network. Thus, inserting guard space as in conventional methods are not needed for uplink channel estimation which removes the required training overhead. Finally, an efficient uplink detector is proposed by taking into account the channel estimation uncertainty. Through numerical simulations, we demonstrate the benefits of the proposed ISAC-assisted OTFS transmission scheme.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 May 2021.
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Citing papers per year: 2022: 2; 2023: 0; 2024: 2.
4 papers.
- 2022Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AIcited by 33
- 2024Non-orthogonal Age-Optimal Information Dissemination in Vehicular Networks: A Meta Multi-Objective Reinforcement Learning Approachcited by 3
- 2022Dynamic Unicast-Multicast Scheduling for Age-Optimal Information Dissemination in Vehicular Networkscited by 1
- 2024CAV-AHDV-CAV: Mitigating Traffic Oscillations for CAVs through a Novel Car-Following Structure and Reinforcement Learningcited by 1
- 2024CAV-AHDV-CAV: Mitigating Traffic Oscillations for CAVs through a Novel Car-Following Structure and Reinforcement Learning23 Jun 2024
- 2024Non-orthogonal Age-Optimal Information Dissemination in Vehicular Networks: A Meta Multi-Objective Reinforcement Learning Approach15 Feb 2024
- 2022Dynamic Unicast-Multicast Scheduling for Age-Optimal Information Dissemination in Vehicular Networks19 Sep 2022
- 2022Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI3 Jul 2022