车联网通信感知一体化研究：现状与发展趋势

程翔; 张浩天; 杨宗辉; 黄子蔚; 李思江; 余安澜

doi:10.11959/j.issn.1000-436x.2022137

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车联网通信感知一体化研究：现状与发展趋势

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- 车联网通信感知一体化研究：现状与发展趋势
- Integrated sensing and communications for Internet of vehicles：current status and development trend
- 通信学报 2022年43卷第8期页码：188-202
- 作者机构：
  
  1. 北京大学电子学院，北京 100871
  2. 北京大学计算机学院，北京 100871
- 作者简介：
  
  [ "程翔（1979- ），男，山东济南人，博士，北京大学博雅特聘教授、博士生导师，主要研究方向为基于数据驱动的智慧网络和网联智能、无线通信信道建模和应用、5G/B5G 智能车联网和多智能体协同理论和技术" ]
  [ "张浩天（2000- ），男，满族，辽宁沈阳人，北京大学博士生，主要研究方向为未来车联网场景下的通信感知一体化与多智能体协同理论和技术" ]
  [ "杨宗辉（2000- ），男，安徽淮南人，北京大学博士生，主要研究方向为未来车联网场景下的通信感知一体化与多智能体协同理论和技术" ]
  [ "黄子蔚（1996- ），男，浙江绍兴人，北京大学博士生，主要研究方向为未来复杂高速移动场景下的无线通信信道的测量与建模" ]
  [ "李思江（1999- ），男，河北秦皇岛人，北京大学博士生，主要研究方向为车联网场景下的多移动智能体的协同定位感知与智能" ]
  [ "余安澜（2000- ），男，安徽合肥人，北京大学博士生，主要研究方向为毫米波信道估计、信号处理、机器学习应用、通信感知一体化等" ]
- 基金信息：
  
  国家重点研发计划基金资助项目;The National Key Research and Development Program of China(2021ZD0112700);国家自然科学基金资助项目;The National Natural Science Foundation of China(62125101)
- DOI：10.11959/j.issn.1000-436x.2022137
  中图分类号： TN92
- 网络首发：2022-08，
  
  纸质出版：2022-08-25
- 稿件说明：
移动端阅览
程翔, 张浩天, 杨宗辉, 等. 车联网通信感知一体化研究：现状与发展趋势[J]. 通信学报, 2022,43(8):188-202.

Xiang CHENG, Haotian ZHANG, Zonghui YANG, et al. Integrated sensing and communications for Internet of vehicles：current status and development trend[J]. Journal on Communications, 2022, 43(8): 188-202.
程翔, 张浩天, 杨宗辉, 等. 车联网通信感知一体化研究：现状与发展趋势[J]. 通信学报, 2022,43(8):188-202. DOI： 10.11959/j.issn.1000-436x.2022137.

Xiang CHENG, Haotian ZHANG, Zonghui YANG, et al. Integrated sensing and communications for Internet of vehicles：current status and development trend[J]. Journal on Communications, 2022, 43(8): 188-202. DOI： 10.11959/j.issn.1000-436x.2022137.

摘要

车联网作为未来智能交通系统中最重要的组成部分，是实现智慧出行、智慧交通的重要技术之一。随着感知与通信两功能的蓬勃发展与开发利用，通信感知的融合设计，即车联网的通信感知一体化技术，成为当下的研究热点，对智能交通系统的发展具有重要意义。首先，定义和区分了车联网通信感知一体化系统的2种融合模型，即功能融合和信号融合。然后，分别针对2种不同的融合模型对现有工作进行了全面的回顾和梳理。最后，提出了车联网通信感知一体化设计的未来发展方向以及面临的技术挑战。

Abstract

The Internet of vehicles

the most important component of intelligent transportation system (ITS) in the future

is one of the most important technologies to achieve smart traffic and convenient travel for the people.With the vigorous development and continuous utilization of sensing and communication functions

the combination of these two functions

that is

integrated sensing and communications (ISAC) technology of vehicular communication networks

has become the current research hotspot and is of great significance to the development of ITS.Firstly

two different models of ISAC system

i.e.

functional ISAC and signaling ISAC were defined and differentiated.Then

for the two different ISAC models

the existing works were reviewed and analyzed comprehensively.Finally

the future development directions and technical challenges of ISAC design in vehicular communication networks were proposed.

关键词

Keywords

references

BOBAN M T , KOUSARIDAS A , MANOLAKIS K , et al . Connected roads of the future:use cases,requirements,and design considerations for vehicle-to-everything communications [J ] . IEEE Vehicular Technology Magazine , 2018 , 13 ( 3 ): 110 - 123 .

董朔朔 , 刘留 , 樊圆圆 , 等 . 车联网信道特性综述 [J ] . 电波科学学报 , 2021 , 36 ( 3 ): 349 - 367 .

DONG S S , LIU L , FAN Y Y , et al . Overview of the channel characteristics of V2X [J ] . Chinese Journal of Radio Science , 2021 , 36 ( 3 ): 349 - 367 .

JU S H , RAPPAPORT T S . Millimeter-wave extended NYUSIM channel model for spatial consistency [C ] // Proceedings of 2018 IEEE Global Communications Conference . Piscataway:IEEE Press , 2018 : 1 - 6 .

ADEMAJ F , SCHWARZ S , GUAN K , et al . Ray-tracing based validation of spatial consistency for geometry-based stochastic channels [C ] // Proceedings of 2018 IEEE 88th Vehicular Technology Conference . Piscataway:IEEE Press , 2018 : 1 - 5 .

CAI X S , ZHANG G J , ZHANG C , et al . Dynamic channel modeling for indoor millimeter-wave propagation channels based on measurements [J ] . IEEE Transactions on Communications , 2020 , 68 ( 9 ): 5878 - 5891 .

3GPP . Technical specification group radio access network; study on scenarios and requirements for next generation access technologies (release 14):TR 38.913 [S ] . 2016 .

DOCOMO N T T . 5G channel model for bands up to 100 GHz [R ] . 2016 .

JAECKEL S , RASCHKOWSKI K , BORNER K , et al . QuaDRiGa-quasi deterministic radio channel generator,user manual and documentation [R ] . 2017 .

CARDONA N . Cooperative radio communications for green smart environments [M ] . Denmark : River Publishers , 2016 .

HUANG Z W , CHENG X , YIN X F . A general 3D non-stationary 6G channel model with time-space consistency [J ] . IEEE Transactions on Communications , 2022 , 70 ( 5 ): 3436 - 3450 .

YE H , LI G Y , JUANG B H . Power of deep learning for channel estimation and signal detection in OFDM systems [J ] . IEEE Wireless Communications Letters , 2018 , 7 ( 1 ): 114 - 117 .

黄源 , 何怡刚 , 吴裕庭 , 等 . 基于深度学习的压缩感知FDD大规模MIMO系统稀疏信道估计算法 [J ] . 通信学报 , 2021 , 42 ( 8 ): 61 - 69 .

HUANG Y , HE Y G , WU Y T , et al . Deep learning for compressed sensing based sparse channel estimation in FDD massive MIMO systems [J ] . Journal on Communications , 2021 , 42 ( 8 ): 61 - 69 .

LIAO Y , HUA Y X , DAI X W , et al . ChanEstNet:a deep learning based channel estimation for high-speed scenarios [C ] // Proceedings of 2019 IEEE International Conference on Communications . Piscataway:IEEE Press , 2019 : 1 - 6 .

GIZZINI A K , CHAFII M , NIMR A , et al . Deep learning based channel estimation schemes for IEEE 802.11p standard [J ] . IEEE Access , 2020 , 8 : 113751 - 113765 .

LIM S H , KIM S , SHIM B , et al . Deep learning-based beam tracking for millimeter-wave communications under mobility [J ] . IEEE Transactions on Communications , 2021 , 69 ( 11 ): 7458 - 7469 .

YANG Y W , GAO F F , XING C W , et al . Deep multimodal learning:merging sensory data for massive MIMO channel prediction [J ] . IEEE Journal on Selected Areas in Communications , 2021 , 39 ( 7 ): 1885 - 1898 .

MA X L , YE H , LI Y . Learning assisted estimation for time- varying channels [C ] // Proceedings of 2018 15th International Symposium on Wireless Communication Systems (ISWCS) . Piscataway:IEEE Press , 2018 : 1 - 5 .

VA V , SHIMIZU T , BANSAL G , et al . Position-aided millimeter wave V2I beam alignment:a learning-to-rank approach [C ] // Proceedings of 2017 IEEE 28th Annual International Symposium on Personal,Indoor,and Mobile Radio Communications . Piscataway:IEEE Press , 2017 : 1 - 5 .

GONZÁLEZ-PRELCIC N , MÉNDEZ-RIAL R , HEATH R W . Radar aided beam alignment in mmWave V2I communications supporting antenna diversity [C ] // Proceedings of 2016 Information Theory and Applications Workshop (ITA) . Piscataway:IEEE Press , 2016 : 1 - 7 .

BRAMBILLA M , NICOLI M , SAVARESI S , et al . Inertial sensor aided mmWave beam tracking to support cooperative autonomous driving [C ] // Proceedings of 2019 IEEE International Conference on Communications Workshops . Piscataway:IEEE Press , 2019 : 1 - 6 .

张璐璐 , 仲伟志 , 张俊杰 , 等 . 基于车辆环境态势感知的毫米波波束跟踪 [J ] . 信号处理 , 2022 , 38 ( 3 ): 457 - 465 .

ZHANG L L , ZHONG W Z , ZHANG J J , et al . Millimeter wave beam tracking based on vehicle environmental situational awareness [J ] . Journal of Signal Processing , 2022 , 38 ( 3 ): 457 - 465 .

CHEN Z M , CAO Z X , HE X Y , et al . DoA and DoD estimation and hybrid beamforming for radar-aided mmWave MIMO vehicular communication systems [J ] . Electronics , 2018 , 7 ( 3 ): 40 .

ALI A , GONZÁLEZ-PRELCIC N , GHOSH A . Millimeter wave V2I beam-training using base-station mounted radar [C ] // Proceedings of 2019 IEEE Radar Conference (RadarConf) . Piscataway:IEEE Press , 2019 : 1 - 5 .

LIU F , YUAN W J , MASOUROS C , et al . Radar-assisted predictive beamforming for vehicular links:communication served by sensing [J ] . IEEE Transactions on Wireless Communications , 2020 , 19 ( 11 ): 7704 - 7719 .

YUAN W J , LIU F , MASOUROS C , et al . Bayesian predictive beamforming for vehicular networks:a low-overhead joint radar-communication approach [J ] . IEEE Transactions on Wireless Communications , 2021 , 20 ( 3 ): 1442 - 1456 .

YING Z H , CUI Y H , MU J S , et al . Particle filter based predictive beamforming for integrated vehicle sensing and communication [C ] // Proceedings of 2021 IEEE 94th Vehicular Technology Conference . Piscataway:IEEE Press , 2021 : 1 - 5 .

YUAN W J , WEI Z Q , LI S Y , et al . Integrated sensing and communication-assisted orthogonal time frequency space transmission for vehicular networks [J ] . IEEE Journal of Selected Topics in Signal Processing , 2021 , 15 ( 6 ): 1515 - 1528 .

MU J S , GONG Y , ZHANG F P , et al . Integrated sensing and communication-enabled predictive beamforming with deep learning in vehicular networks [J ] . IEEE Communications Letters , 2021 , 25 ( 10 ): 3301 - 3304 .

LIU F , MASOUROS C . A tutorial on joint radar and communication transmission for vehicular networks—part III:predictive beamforming without state models [J ] . IEEE Communications Letters , 2021 , 25 ( 2 ): 332 - 336 .

SHAHAM S , DING M , KOKSHOORN M , et al . Fast channel estimation and beam tracking for millimeter wave vehicular communications [J ] . IEEE Access , 2019 , 7 : 141104 - 141118 .

MUNS G R , MISHRA K V , GUERRA C B , et al . Beam alignment and tracking for autonomous vehicular communication using IEEE 802.11ad-based radar [C ] // Proceedings of IEEE Conference on Computer Communications Workshops . Piscataway:IEEE Press , 2019 : 535 - 540 .

XU Y H , GUO Y H , LI C , et al . Predictive beam tracking with cooperative sensing for vehicle-to-infrastructure communications [C ] // Proceedings of 2021 IEEE/CIC International Conference on Communications in China (ICCC) . Piscataway:IEEE Press , 2021 : 835 - 840 .

KLAUTAU A , GONZÁLEZ-PRELCIC N , HEATH R W . LiDAR data for deep learning-based mmWave beam-selection [J ] . IEEE Wireless Communications Letters , 2019 , 8 ( 3 ): 909 - 912 .

NISHIO T , ARAI R , YAMAMOTO K , et al . Proactive traffic control based on human blockage prediction using RGBD cameras for millimeter-wave communications [C ] // Proceedings of 2015 12th Annual IEEE Consumer Communications and Networking Conference . Piscataway:IEEE Press , 2015 : 152 - 153 .

ALRABEIAH M , HREDZAK A , ALKHATEEB A . Millimeter wave base stations with cameras:vision-aided beam and blockage prediction [C ] // Proceedings of 2020 IEEE 91st Vehicular Technology Conference . Piscataway:IEEE Press , 2020 : 1 - 5 .

CHARAN G , ALRABEIAH M , ALKHATEEB A . Vision-aided 6G wireless communications:blockage prediction and proactive handoff [J ] . IEEE Transactions on Vehicular Technology , 2021 , 70 ( 10 ): 10193 - 10208 .

DIAS M , KLAUTAU A , GONZÁLEZ-PRELCIC N , et al . Position and LiDAR-aided mmWave beam selection using deep learning [C ] // Proceedings of 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications . Piscataway:IEEE Press , 2019 : 1 - 5 .

LI H , NASHASHIBI F . Multi-vehicle cooperative perception and augmented reality for driver assistance:a possibility to ‘see’ through front vehicle [C ] // Proceedings of 2011 14th International IEEE Conference on Intelligent Transportation Systems . Piscataway:IEEE Press , 2011 : 242 - 247 .

KIM S W , QIN B X , CHONG Z J , et al . Multivehicle cooperative driving using cooperative perception:design and experimental validation [J ] . IEEE Transactions on Intelligent Transportation Systems , 2015 , 16 ( 2 ): 663 - 680 .

CHEN Q , TANG S H , YANG Q , et al . Cooper:cooperative perception for connected autonomous vehicles based on 3D point clouds [C ] // Proceedings of 2019 IEEE 39th International Conference on Distributed Computing Systems . Piscataway:IEEE Press , 2019 : 514 - 524 .

WANG T H , MANIVASAGAM S , LIANG M , et al . V2VNet:vehicle-to-vehicle communication for joint perception and prediction [C ] // Computer Vision - ECCV 2020 . Berlin:Springer , 2020 : 605 - 621 .

CHEN Q , MA X , TANG S H , et al . F-cooper:feature based cooperative perception for autonomous vehicle edge computing system using 3D point clouds [C ] // Proceedings of the 4th ACM/IEEE Symposium on Edge Computing . New York:ACM Press , 2019 : 88 - 100 .

XIAO Z Y , MO Z B , JIANG K , et al . Multimedia fusion at semantic level in vehicle cooperactive perception [C ] // Proceedings of 2018 IEEE International Conference on Multimedia ＆ Expo Workshops . Piscataway:IEEE Press , 2018 : 1 - 6 .

RAWASHDEH Z Y , WANG Z . Collaborative automated driving:a machine learning-based method to enhance the accuracy of shared information [C ] // Proceedings of 2018 21st International Conference on Intelligent Transportation Systems (ITSC) . Piscataway:IEEE Press , 2018 : 3961 - 3966 .

CHO H , SEO Y W , KUMAR B V K V , et al . A multi-sensor fusion system for moving object detection and tracking in urban driving environments [C ] // Proceedings of 2014 IEEE International Conference on Robotics and Automation . Piscataway:IEEE Press , 2014 : 1836 - 1843 .

安鑫 , 蔡伯根 , 上官伟 . 车路协同路侧感知融合方法的研究 [J ] . 测控技术 , 2022 , 41 ( 2 ): 1 - 12 , 35 .

AN X , CAI B G , SHANGGUAN W . Vehicle Road cooperative roadside perception fusion method [J ] . Measurement ＆ Control Technology , 2022 , 41 ( 2 ): 1 - 12 , 35 .

TAVIK G C , HILTERBRICK C L , EVINS J B , et al . The advanced multifunction RF concept [J ] . IEEE Transactions on Microwave Theory and Techniques , 2005 , 53 ( 3 ): 1009 - 1020 .

LIU Y J , LIAO G S , YANG Z W , et al . Multiobjective optimal waveform design for OFDM integrated radar and communication systems [J ] . Signal Processing , 2017 , 141 : 331 - 342 .

刘永军 , 廖桂生 , 杨志伟 , 等 . 一种超分辨OFDM雷达通信一体化设计方法 [J ] . 电子与信息学报 , 2016 , 38 ( 2 ): 425 - 433 .

LIU Y J , LIAO G S , YANG Z W , et al . A super-resolution design method for integration of OFDM radar and communication [J ] . Journal of Electronics ＆ Information Technology , 2016 , 38 ( 2 ): 425 - 433 .

HASSANIEN A , AMIN M G , ZHANG Y D , et al . Dual-function radar-communications:information embedding using sidelobe control and waveform diversity [J ] . IEEE Transactions on Signal Processing , 2016 , 64 ( 8 ): 2168 - 2181 .

LIU F , ZHOU L F , MASOUROS C , et al . Toward dual-functional radar-communication systems:optimal waveform design [J ] . IEEE Transactions on Signal Processing , 2018 , 66 ( 16 ): 4264 - 4279 .

LIU F , MASOUROS C , LI A , et al . MU-MIMO communications with MIMO radar:from co-existence to joint transmission [J ] . IEEE Transactions on Wireless Communications , 2018 , 17 ( 4 ): 2755 - 2770 .

曾浩 , 吉利霞 , 李凤 , 等 . 16QAM-LFM 雷达通信一体化信号设计 [J ] . 通信学报 , 2020 , 41 ( 3 ): 182 - 189 .

ZENG H , JI L X , LI F , et al . 16QAM-LFM waveform design for integrated radar and communication [J ] . Journal on Communications , 2020 , 41 ( 3 ): 182 - 189 .

李晓柏 , 杨瑞娟 , 陈新永 , 等 . 基于分数阶傅里叶变换的雷达通信一体化信号共享研究 [J ] . 信号处理 , 2012 , 28 ( 4 ): 487 - 494 .

LI X B , YANG R J , CHEN X Y , et al . The sharing signal for integrated radar and communication based on FRFT [J ] . Signal Processing , 2012 , 28 ( 4 ): 487 - 494 .

HUANG T Y , SHLEZINGER N , XU X Y , et al . MAJoRCom:a dual-function radar communication system using index modulation [J ] . IEEE Transactions on Signal Processing , 2020 , 68 : 3423 - 3438 .

刘永军 , 廖桂生 , 杨志伟 . OFDM 雷达通信一体化波形相参积累研究 [J ] . 信号处理 , 2017 , 33 ( 3 ): 253 - 259 .

LIU Y J , LIAO G S , YANG Z W . A study for the coherent integration with integrated radar and communication waveform based on OFDM [J ] . Journal of Signal Processing , 2017 , 33 ( 3 ): 253 - 259 .

REICHARDT L , STURM C , GRÜNHAUPT F , et al . Demonstrating the use of the IEEE 802.11P Car-to-Car communication standard for automotive radar [C ] // Proceedings of 2012 6th European Conference on Antennas and Propagation (EUCAP) . Piscataway:IEEE Press , 2012 : 1576 - 1580 .

KUMARI P , CHOI J , GONZÁLEZ-PRELCIC N , , et al . IEEE 802.11ad-based radar:an approach to joint vehicular communication-radar system [J ] . IEEE Transactions on Vehicular Technology , 2018 , 67 ( 4 ): 3012 - 3027 .

GROSSI E , LOPS M , VENTURINO L , et al . Opportunistic automotive radar using the IEEE 802.11ad standard [C ] // Proceedings of 2017 IEEE Radar Conference (RadarConf) . Piscataway:IEEE Press , 2017 : 1196 - 1200 .

KUMARI P , GONZALEZ-PRELCIC N , HEATH R W . Investigating the IEEE 802.11ad standard for millimeter wave automotive radar [C ] // Proceedings of 2015 IEEE 82nd Vehicular Technology Conference . Piscataway:IEEE Press , 2015 : 1 - 5 .

DANIELS R C , YEH E R , HEATH R W . Forward collision vehicular radar with IEEE 802.11:feasibility demonstration through measurements [J ] . IEEE Transactions on Vehicular Technology , 2018 , 67 ( 2 ): 1404 - 1416 .

HOSSEIN D S , BHAVANI M R , STIFTER T , et al . Multicarrier phase modulated continuous waveform for automotive joint radar-communication system [C ] // Proceedings of 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications . Piscataway:IEEE Press , 2018 : 1 - 5 .

DOKHANCHI S H , SHANKAR M R B , NIJSURE Y A , et al . Joint automotive radar-communications waveform design [C ] // Proceedings of 2017 IEEE 28th Annual International Symposium on Personal,Indoor,and Mobile Radio Communications . Piscataway:IEEE Press , 2017 : 1 - 7 .

DOKHANCHI S H , SHANKAR M R B , STIFTER T , et al . OFDM-based automotive joint radar-communication system [C ] // Proceedings of 2018 IEEE Radar Conference (RadarConf18) . Piscataway:IEEE Press , 2018 : 902 - 907 .

DOKHANCHI S H , MYSORE B S , MISHRA K V , et al . A mmWave automotive joint radar-communications system [J ] . IEEE Transactions on Aerospace and Electronic Systems , 2019 , 55 ( 3 ): 1241 - 1260 .

LIU F , MASOUROS C . Joint localization and predictive beamforming in vehicular networks:power allocation beyond water-filling [C ] // Proceedings of ICASSP 2021 - 2021 IEEE International Conference on Acoustics,Speech and Signal Processing . Piscataway:IEEE Press , 2021 : 8393 - 8397 .

JIANG N , HUANG S , ZHAO Y F , et al . Joint power control and time allocation for joint radar and communication system in cognitive radio enabled vehicular ad-hoc networks [C ] // Proceedings of 2019 6th International Conference on Dependable Systems and Their Applications (DSA) . Piscataway:IEEE Press , 2020 : 470 - 474 .

CHIRIYATH A R , PAUL B , JACYNA G M , et al . Inner bounds on performance of radar and communications co-existence [J ] . IEEE Transactions on Signal Processing , 2016 , 64 ( 2 ): 464 - 474 .

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