1.北京科技大学计算机与通信工程学院,北京 100083
2.北京市科学技术研究院,北京 100094
[ "孙春蕾(1992‒ ),女,博士,北京科技大学计算机与通信工程学院讲师,北京市科学技术协会青年托举人才,主要研究方向为通信感知一体化、B5G/6G、车联网。" ]
[ "卢祉伊(2007‒ ),女,北京科技大学计算机与通信工程学院在读,主要研究方向为通信与感知一体化、无线通信。" ]
[ "刘旭卿(1992‒ ),男,博士,北京市科学技术研究院研究员,主要研究方向为智能感知、大语言模型应用、多模态大模型。" ]
[ "丁佳伟(2007‒ ),男,北京科技大学计算机与通信工程学院在读,主要研究方向为通信与感知一体化、无线通信。" ]
[ "张海君(1986‒ ),男,博士,北京科技大学计算机与通信工程学院教授,国家杰出青年科学基金获得者、IEEE Fellow,智能科学与技术学院院长、中国电子学会理事、中国通信学会理事/会士、中国电子学会青年科学家俱乐部轮值主席、中国通信学会青年工作委员会副主任委员。主要研究方向为6G网络、通信感知一体化网络、卫星网络。" ]
收稿:2025-12-10,
修回:2025-12-30,
录用:2026-01-13,
纸质出版:2026-03-30
移动端阅览
孙春蕾,卢祉伊,刘旭卿等.基于B5G/6G参考信号的通信感知一体化机制[J].物联网学报,2026,10(01):99-111.
Sun Chunlei,Lu Zhiyi,Liu Xuqing,et al.Integrated sensing and communication mechanism based on B5G/6G reference signals[J].Chinese Journal on Internet of Things,2026,10(01):99-111.
孙春蕾,卢祉伊,刘旭卿等.基于B5G/6G参考信号的通信感知一体化机制[J].物联网学报,2026,10(01):99-111. DOI: 10.11959/j.issn.2096-3750.2026.00549.
Sun Chunlei,Lu Zhiyi,Liu Xuqing,et al.Integrated sensing and communication mechanism based on B5G/6G reference signals[J].Chinese Journal on Internet of Things,2026,10(01):99-111. DOI: 10.11959/j.issn.2096-3750.2026.00549.
通信感知一体化(ISAC
integrated sensing and communication)作为未来6G的关键技术之一,旨在赋予网络智能化感知能力,有效地支撑智能交通、数字孪生等创新应用。然而,现有基于参考信号(RS
reference signal)的ISAC方案受限于信号稀疏、信噪比低、以及梳状频谱引发的距离-速度模糊等问题,难以兼顾感知精度、覆盖范围与通信服务质量(QoS
communication quality of service)。聚焦基于B5G/6G参考信号的ISAC机制,针对现有系统中参考信号资源稀疏导致的感知覆盖与分辨率受限的问题,提出多类信号融合与网络化协同的ISAC架构,在保障通信用户服务质量的前提下增强系统感知能力;在此基础上,面向低信噪比(SNR
signal-to-noise ratio)、小样本观测以及梳状频谱引发的距离-速度模糊等挑战,设计高精度、鲁棒的目标参数估计算法,有效地提升通信系统感知性能。验证了基于参考信号实现高效ISAC的可行性,为6G通感融合系统提供了兼具工程实用性与理论价值的技术路径。
Integrated sensing and communication (ISAC) is a key enabler for 6G
aiming to equip wireless networks with sensing capabilities to support applications
such as intelligent transportation and digital twins. However
the present reference signal (RS)-based ISAC mechanisms are unable to strike a balance among sensing accuracy
coverage
and communication quality of service (QoS). The ISAC schemes based on B5G/6G RSs were investigated in this paper. Firstly
a multi-signal fusion and cooperative architecture was proposed to overcome sparse signal placement and enhance sensing coverage and resolution without degrading communication quality. On this basis
a robust parameter estimation algorithm was developed to address challenges including low signal-to-noise ratio (SNR)
limited observation samples
and range-velocity ambiguity
significantly improving sensing performance. This demonstrates the feasibility of efficient RS-based ISAC strategies
providing a technically viable and theoretically sound approach for the integration of communication and sensing in 6G.
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