With radar cooperative sensing, vehicles can not only leverage their own radar to perceive the road condition ahead but also establish a connection with vehicles in front to obtain environment information farther away. In this paper, a radar cooperative sensing scheme based on multi-hop for vehicular networks is proposed for far-reaching perception. Vehicles on the road perform radar sensing while conducting multi-hop communication with front ones to obtain road information outside the LOS range. In order to streamline hardware equipment and deal with the upcoming shortage of spectrum resources, each vehicle is equipped with TD-JRC to realize both radar sensing and communication function within the same frequency band. Besides, we design a resource allocation strategy for this cooperative sensing system, numerical and simulation results show that there is indeed a optimal joint power and time allocation strategy to realize the maximized average RCSCR for a definite vehicle density.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Perception-Connection Tradeoff for Radar Cooperative Sensing in Multi-hop Vehicular Networks


    Weitere Titelangaben:

    Lect.Notes Social.Inform.


    Beteiligte:
    Shi, Shuo (Herausgeber:in) / Ma, Ruofei (Herausgeber:in) / Lu, Weidang (Herausgeber:in) / Wang, Mingyi (Autor:in) / Ma, Ruofei (Autor:in) / Kang, Wenjing (Autor:in) / Liu, Gongliang (Autor:in)

    Kongress:

    International Conference on 5G for Future Wireless Networks ; 2021 ; Huizhou, China October 30, 2021 - October 31, 2021



    Erscheinungsdatum :

    05.05.2022


    Format / Umfang :

    14 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Cooperative perception in vehicular networks using multi-agent reinforcement learning

    Abdel-Aziz, M. K. (Mohamed K.) / Samarakoon, S. (Sumudu) / Perfecto, C. (Cristina) et al. | BASE | 2021

    Freier Zugriff

    Enhancing Cooperative LiDAR-Based Perception Accuracy in Vehicular Edge Networks

    Hou, Jiawei / Yang, Peng / Dai, Xiangxiang et al. | IEEE | 2025


    AoI Analysis for Automatic Repeat-Request in Vehicular Cooperative Perception Networks

    Huang, Qinan / Zeng, Jianhua / Guo, Chongtao et al. | IEEE | 2024



    Cooperative Multi-sensor Multi-vehicle Localization in Vehicular Adhoc Networks

    Goli, Sepideh Afkhami / Far, Behrouz H. / Fapojuwo, Abraham O. | IEEE | 2015