The development of dual-functional radar-communication (DFRC) systems, where vehicle localization and tracking can be combined with vehicular communication, will lead to more efficient future vehicular networks. In this paper, we develop a predictive beamforming scheme in the context of DFRC systems. We consider a system model where the road-side unit estimates and predicts the motion parameters of vehicles based on the echoes of the DFRC signal. Compared to the conventional feedback-based beam tracking approaches, the proposed method can reduce the signaling overhead and improve the accuracy of the angle estimation. To accurately estimate the motion parameters of vehicles in real-time, we propose a novel message passing algorithm based on factor graph, which yields a near optimal performance achieved by the maximum a posteriori estimation. The beamformers are then designed based on the predicted angles for establishing the communication links. With the employment of appropriate approximations, all messages on the factor graph can be derived in a closed-form, thus reduce the complexity. Simulation results show that the proposed DFRC based beamforming scheme is superior to the feedback-based approach in terms of both estimation and communication performance. Moreover, the proposed message passing algorithm achieves a similar performance of the high-complexity particle filtering-based methods.


    Access

    Download


    Export, share and cite



    Title :

    Bayesian Predictive Beamforming for Vehicular Networks: A Low-Overhead Joint Radar-Communication Approach


    Contributors:
    Yuan, W (author) / Liu, F (author) / Masouros, C (author) / Yuan, J (author) / Ng, DWK (author) / González-Prelcic, N (author)

    Publication date :

    2021-03-01


    Remarks:

    IEEE Transactions on Wireless Communications , 20 (3) pp. 1442-1456. (2021)


    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    DDC:    629



    Radar-Assisted Predictive Beamforming for Vehicular Links: Communication Served by Sensing

    Liu, F / Yuan, W / Masouros, C et al. | BASE | 2020

    Free access

    R-Comm: A Traffic Based Approach for Joint Vehicular Radar-Communication

    Singh, Rohit / Saluja, Deepak / Kumar, Suman | IEEE | 2022


    An Iterative Joint Tx-Rx Hybrid Beamforming Method for Vehicular Networks

    Li, Yunda / Zhao, Le / Sun, Chen et al. | IEEE | 2023


    Joint Beamforming and Phase Shift Design for IRS-Aided Vehicular Networks

    Cui, Yaping / Wang, Gongxun / He, Peng et al. | IEEE | 2023


    Joint Radar-Communication Beamforming for CRB-Based Target Localization

    Mao, Tianhao / Yang, Jie / Liang, Le et al. | IEEE | 2024