In this chapter, we study the employment of integrated sensing and communication (ISAC) technology in vehicular networks, where vehicle tracking and vehicular communications can be combined for improving the overall system throughput. In the context of ISAC system, we develop a novel predictive beamforming scheme. In particular, the road-side unit (RSU) estimates and predicts the motion parameters of vehicles based on the echoes of the ISAC signal, which addresses the limitations of the conventional feedback-based beam tracking approaches, such as the high signaling overhead and low accuracy of angle estimation. A novel Bayesian inference scheme is proposed based on the vehicle state evolution model. Considering that the point-target assumption is impractical, we introduce the extended target model. Then, the beamwidth is adjusted in real-time to cover the entire vehicle. In addition, to improve the tracking accuracy and communication quality of service (QoS), we model the complicated roadway geometry via curvilinear coordinate system (CCS) and develop an interacting multiple model extended Kalman filter (IMM-EKF) framework. An optimization problem is formulated to maximize the array gain through dynamically adjusting the array size and thereby controlling the beamwidth, which takes the performance loss caused by beam misalignment into account. Numerical results have demonstrated the effectiveness of our proposed ISAC techniques for supporting vehicular communications.


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    Title :

    Integrated Sensing and Communication for Vehicular Networks


    Contributors:
    Liu, Fan (editor) / Masouros, Christos (editor) / Eldar, Yonina C. (editor) / Yuan, Weijie (author) / Du, Zhen (author) / Meng, Xiao (author) / Liu, Fan (author) / Masouros, Christos (author)

    Published in:

    Publication date :

    2023-07-19


    Size :

    26 pages




    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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