通信感知一体化原型验证的研究现状与发展趋势

doi:10.11959/j.issn.1000-436x.2023205

Abstract

Abstract:

Considering the practical challenges faced by the commercialization of integrated sensing and communication (ISAC), the implementation progress and future trends of ISAC were elaborated and summarized.First, the research progress and empirical performance of single node-based ISAC prototype systems were summarized, including positioning, environmental mapping, imaging, waveform design, and beam tracking.Next, the actual gain of multi-nodes cooperation-based ISAC prototype systems in the scenarios of the Internet of vehicles and the Internet of things were analyzed.Then, focusing on cross-sensor-based ISAC prototype systems, the advantages and disadvantages of each kind of sensor and the experimental fusion performance were overviewed.Finally, the future trends in the system implementation and technical validation of ISAC were highlighted, addressing the challenges encountered in existing ISAC prototype systems.

Key words: integrated sensing and communication, prototype verification, localization, environmental mapping, imaging, waveform design, beam tracking

CLC Number:

TN92

Jie YANG, Yixuan HUANG, Tao DU, Hang QUE, Shuqiang XIA, Shi JIN. Prototype verification for integrated sensing and communications:current status and development trends[J]. Journal on Communications, 2023, 44(11): 43-54.

Figures/Tables 9

References 58

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文献	工作频段/GHz	工作带宽/MHz	天线配置	波形	工作方式	实测参数	平均定位误差
文献[9]	5.1	80	4 × 1均匀线阵	啁啾	自发自收	角度	—
文献[10]	2.35	40	BS：单天线	任意	上行定位	TDOA	0.42 m（100 m²暗室）
			UE：矢量信号分析仪
文献[11]	77	2 000	les[J].IEEE Internet 单喇叭天线模拟29天线）	单载波Zadoff-Chu	自发自收	距离角度图	—
文献[12]	27.2	400	BS：16×24均匀面阵	OFDM	下行定位	AOA AOD TOA	24.82 m（10⁵m²室外）
			UE：2 × 8均匀面阵

文献	工作频段/ GHz	工作带宽/ MHz	天线配置	波形	工作方式	实测参数	环境制图精度
文献[13]	27.2	400	BS：16 × 24均匀面阵	OFDM	下行	AOA、AOD（模拟RTT）	2 m（10⁵m²室外）
			UE：2 × 8均匀面阵
文献[14]	28	400	UE：2个喇叭天线	OFDM	自发自收	距离角度图	2 m（50 m长室内走廊）

文献	工作频段/ GHz	成像技术	实测效果
文献[19]	220～300	SAR成像	2D同时用户定位和反射面成像
文献[20]	3～19.5	SAR成像	3D目标散射系数图像
文献[21]	30～36	SAR成像	3D目标散射系数图像
文献[22]	140	压缩感知辅助的SAR成像	3D目标散射系数图像
文献[23]	28～29	基于波束成形的雷达测距	3D测距深度图，测距精度15 cm
文献[24]	9.2	基于RIS的线性逆散射求解	2D金属贴片成像
文献[25]	3.198	基于RIS的线性逆散射求解	2D空间点云成像

设备类型	输出数据	特点	文献	实现功能	所提方案	预测效果
毫米波通信系统	毫米波接收信号	与电磁波传输环境高度耦合，但路径损耗高且易受阻挡	文献[33]	阻隔预测	无线指纹提取+RNN	20 ms内阻隔遮挡预测成功率高于85%
GPS	目标经纬坐标	室外实现粗略定位，但室内通常失效	文献[37]	波束预测	神经网络	相比穷举算法，波束训练开销降低70%
			文献[36]	阻隔预测	MIRNet+深度神经网络	1 s内阻隔遮挡预测成功率约为80%
相机	RGB图像	包含丰富环境信息，但易受光线条件影响	文献[40]	波束预测	自编码器网络	相比穷举算法，波束训练开销降低99%
			文献[41]	波束预测	语义信息提取+神经网络	相比直接使用RGB图像，网络参数量降低约83%
			文献[42]	波束预测	离线预测+在线追踪	相比使用离线方案，波束预测精度提升约15%
FMCW雷达	雷达回波信号	检测目标范围广，但易受射频干扰影响	文献[35]	阻隔预测	CNN	1 s内阻隔遮挡预测成功率高于90%
			文献[38]	波束预测	深度神经网络	相比穷举算法，波束训练开销降低93%
LiDAR	激光点云	检测目标精度高，但易受雨雾天气影响	文献[34]	阻隔预测	CNN	1 s内阻隔遮挡预测成功率相比无LiDAR提升45%
			文献[39]	波束预测	RNN	相比穷举算法，波束训练开销降低90%

Prototype verification for integrated sensing and communications:current status and development trends

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