车联网通信感知一体化研究：现状与发展趋势

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

通信学报 ›› 2022, Vol. 43 ›› Issue (8): 188-202.doi: 10.11959/j.issn.1000-436x.2022137

车联网通信感知一体化研究：现状与发展趋势

程翔¹, 张浩天¹, 杨宗辉¹, 黄子蔚¹, 李思江¹, 余安澜²

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

修回日期:2022-06-10 出版日期:2022-08-25 发布日期:2022-08-01
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
基金资助:
国家重点研发计划基金资助项目(2021ZD0112700);国家自然科学基金资助项目(62125101)

Integrated sensing and communications for Internet of vehicles：current status and development trend

Xiang CHENG¹, Haotian ZHANG¹, Zonghui YANG¹, Ziwei HUANG¹, Sijiang LI¹, Anlan YU²

¹ School of Electronics, Peking University, Beijing 100871, China
² School of Computer Science, Peking University, Beijing 100871, China

Revised:2022-06-10 Online:2022-08-25 Published:2022-08-01
Supported by:
The National Key Research and Development Program of China(2021ZD0112700);The National Natural Science Foundation of China(62125101)

摘要/Abstract

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。

关键词: 车联网, 通信感知一体化, 功能融合, 信号融合

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.

Key words: Internet of vehicles, ISAC, functional ISAC, signaling ISAC

中图分类号:

TN92

程翔, 张浩天, 杨宗辉, 黄子蔚, 李思江, 余安澜. 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, Ziwei HUANG, Sijiang LI, Anlan YU. Integrated sensing and communications for Internet of vehicles：current status and development trend[J]. Journal on Communications, 2022, 43(8): 188-202.

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文献	建模方法	捕捉方法	一致性		描述
文献	建模方法	捕捉方法	时间	空间	描述
文献[3-5]	GBDM	几何法	√	×	几何散射环境的表征
文献[6]	GBSM	参数法	√	×	基于相关距离的时间一致信道参数生成
文献[7]	GBSM	参数法	√	×	基于网格的时间一致信道参数生成
文献[8]	GBSM	参数法	√	×	平方正弦数学因子
文献[9]	GBSM	几何法	√	×	可视区域
文献[10]	GBSM	混合法	√	√	基于一致连续性和有界变差理论的可视因子，空时域可视区域的构建

文献	波束训练	波束追踪	波束成形预测	是否需要导频信号	感知设备类型	通信开销	是否使用估计值	应用场景
文献[18]	√	×	×	√	—	大	否	不限
文献[19]	√	×	×	√	雷达	大	否	不限
文献[20]	×	√	×	√	惯性传感器	大	否	不限
文献[24-27]	×	×	√	×	雷达	小	是	直线路径
文献[28]	×	×	√	×	雷达	小	否	直线路径
文献[29]	×	×	√	×	雷达	小	是	不限
文献[30]	×	√	×	√	—	大	是	直线路径
文献[31]	×	√	×	×	雷达	小	否	不限
文献[32]	×	√	×	√	雷达	大	是	不限
文献[33]	√	×	×	√	激光雷达	大	否	不限

文献	协同类型	交互内容	通信负担	特点
文献[38-40]	数据级	传感器数据或原始地图	大	信息完整，精度高，计算负担重
文献[41-42]	特征级	神经网络中间数据	中	精度和效率居中，特征具体形式变化大
文献[43-46]	语义级	目标语义或语义分割	小	信息抽象，效率高，可能丢失信息

融合方式	是否共享频谱资源	是否共享硬件资源	融合方式	信号设计核心	主要研究问题	融合程度	可应用场景
正交融合	否	否	通信感知信息交换	设计干扰消除与管理技术	干扰信道设计；发射机设计；接收抗干扰设计	较低	较有限
非正交融合	是	是	通信感知硬件共享、协同工作	设计一体化资源分配方案	一体化信息论；一体化资源分配；一体化协议及架构	较高	较丰富
机会主义融合	是	是	通信感知硬件共享、协同工作	设计一体化信号处理方案	一体化信号处理；参数估计优化与频谱效率提升	较高	较丰富

设计方法	代表文献	是否区分功能主体	融合程度	方法描述	计算复杂度	双功能性能提升程度	方法局限	车联网适用程度
嵌入共存	文献[62-63]	否	高	通信与雷达波形同时嵌入并处理	较低	较高	雷达通信双功能之间存在相互干扰	较适用
联合优化	文献[64-65]	否	高	通信与感知性能权衡与联合优化	较高	高	优化目标与严格理论性能界尚不明确	较适用

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

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融合模型	研究方向	具体研究内容	未来发展方向	面临挑战
		信道建模	在准确性和复杂度之间有更好折中的信道建模方法	适用于车联网通信感知一体化系统的新颖信道建模方法的构建
	感知功能辅助通信	信道估计	多模态感知环境信息的深度学习理解性融合与更高的分辨率精度	复杂多场景下信道估计算法的泛化能力与适应性
功能融合		V2I波束对准	感知信息辅助下实现更细粒度的车辆状态追踪	车辆高速移动的通信接收机及相对 RSU快速变化的尺寸对波束覆盖范围的影响
		资源联合分配	跨大规模高动态车联网的多维度资源联合优化分配	通过感知功能辅助探测多车辆的任务时空分布特征以及未来变化趋势
	通信功能支撑感知	超视距感知	针对周边环境特征设计的多级分层的协同超视距感知方法	感知信息精度和通信负担以及处理复杂度的最优化权衡
	物理层优化设计	专用于车联网的波形设计	考虑通信感知波形的深层融合，充分探索双功能的耦合	车联网场景的高速移动性与低时延高可靠的传输指标等增加了波形设计的难度
信号融合		统一实用的性能界度量	形成通信与感知相统一的实用可靠的性能度量	需要进一步揭示通信与感知的信息论之间的联系
	跨层联合优化设计	适用于车联网的系统架构	对场景与工作模式进行细化，信号与功能融合的相互辅助与结合	车联网场景复杂且高动态性，简单的架构难以覆盖所有路况

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