ISAC-Enabled V2I Networks Based on 5G NR: How Much Can the Overhead Be Reduced?

被引:10
作者
Li, Yunxin [1 ]
Liu, Fan [1 ]
Du, Zhen [1 ,2 ]
Yuan, Weijie [1 ]
Masouros, Christos [3 ]
机构
[1] Southern Univ Sci & Technol, Shenzhen, Peoples R China
[2] Nanjing Univ Informat Sci & Technol, Nanjing, Peoples R China
[3] UCL, London, England
来源
2023 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS WORKSHOPS, ICC WORKSHOPS | 2023年
关键词
V2I; ISAC; 5G NR; EKF; beam tracking; RADAR;
D O I
10.1109/ICCWORKSHOPS57953.2023.10283528
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The emergence of the fifth-generation (5G) New Radio (NR) brings additional possibilities to vehicle-to-everything (V2X) network with improved quality of services. In order to obtain accurate channel state information (CSI) in highmobility V2X networks, pilot signals and frequent handover between vehicles and infrastructures are required to establish and maintain the communication link, which increases the overheads and reduces the communication throughput. To address this issue, integrated sensing and communications (ISAC) was employed at the base station (BS) in the vehicle-to-infrastructure (V2I) network to reduce a certain amount of overheads, thus improve the spectral efficiency. Nevertheless, the exact amount of overheads reduction remains unclear, particularly for practical NR based V2X networks. In this paper, we study a link-level NR based V2I system employing ISAC signaling to facilitate the communication beam management, where the Extended Kalman filtering (EKF) algorithm is performed to realize the functions of tracking and predicting the motion of the vehicle. We provide detailed analysis on the overheads reduction with the aid of ISAC, and show that up to 43.24% overheads can be reduced under assigned NR frame structure. In addition, numerical results are provided to validate the improved performance on the beam tracking and communication throughput.
引用
收藏
页码:691 / 696
页数:6
相关论文
共 10 条
[1]  
3GPP, 2022, 3GPP TS 38.211, V16.10.0
[2]  
3rd Generation Partnership Project (3GPP), 2017, Technical Report 38.802
[3]  
[Anonymous], 2017, 3GPP TR 38.804 V1.0.0
[4]   Integrated Sensing and Communications for V2I Networks: Dynamic Predictive Beamforming for Extended Vehicle Targets [J].
Du, Zhen ;
Liu, Fan ;
Yuan, Weijie ;
Masouros, Christos ;
Zhang, Zenghui ;
Xia, Shuqiang ;
Caire, Giuseppe .
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2023, 22 (06) :3612-3627
[5]   A Survey on Fundamental Limits of Integrated Sensing and Communication [J].
Liu, An ;
Huang, Zhe ;
Li, Min ;
Wan, Yubo ;
Li, Wenrui ;
Han, Tony Xiao ;
Liu, Chenchen ;
Du, Rui ;
Tan, Danny Kai Pin ;
Lu, Jianmin ;
Shen, Yuan ;
Colone, Fabiola ;
Chetty, Kevin .
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2022, 24 (02) :994-1034
[6]   Radar-Assisted Predictive Beamforming for Vehicular Links: Communication Served by Sensing [J].
Liu, Fan ;
Yuan, Weijie ;
Masouros, Christos ;
Yuan, Jinhong .
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2020, 19 (11) :7704-7719
[7]   Joint Radar and Communication Design: Applications, State-of-the-Art, and the Road Ahead [J].
Liu, Fan ;
Masouros, Christos ;
Petropulu, Athina P. ;
Griffiths, Hugh ;
Hanzo, Lajos .
IEEE TRANSACTIONS ON COMMUNICATIONS, 2020, 68 (06) :3834-3862
[8]  
Meng X., 2022, arXiv
[9]   5G MM WAVE POSITIONING FOR VEHICULAR NETWORKS [J].
Wymeersch, Henk ;
Seco-Granados, Gonzalo ;
Destino, Giuseppe ;
Dardari, Davide ;
Tufvesson, Fredrik .
IEEE WIRELESS COMMUNICATIONS, 2017, 24 (06) :80-86
[10]   Bayesian Predictive Beamforming for Vehicular Networks: A Low-Overhead Joint Radar-Communication Approach [J].
Yuan, Weijie ;
Liu, Fan ;
Masouros, Christos ;
Yuan, Jinhong ;
Ng, Derrick Wing Kwan ;
Gonzalez-Prelcic, Nuria .
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2021, 20 (03) :1442-1456