Integrated Sensing and Communications Framework for 6G: Key Technologies and Hardware Prototype Validation

ZHAO Chuanbin; SUN Hong; ZHANG Tengyu; LUO Hongliang; WANG Yucong; JIANG Yuhua; LIN Bo; GAO Feifei

doi:10.11999/JEIT241114

Volume 47 Issue 4

Apr. 2025

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Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(4): 932-947

ZHAO Chuanbin, SUN Hong, ZHANG Tengyu, LUO Hongliang, WANG Yucong, JIANG Yuhua, LIN Bo, GAO Feifei. Integrated Sensing and Communications Framework for 6G: Key Technologies and Hardware Prototype Validation[J]. Journal of Electronics & Information Technology, 2025, 47(4): 932-947. doi: 10.11999/JEIT241114

Citation:

ZHAO Chuanbin, SUN Hong, ZHANG Tengyu, LUO Hongliang, WANG Yucong, JIANG Yuhua, LIN Bo, GAO Feifei. Integrated Sensing and Communications Framework for 6G: Key Technologies and Hardware Prototype Validation[J]. Journal of Electronics & Information Technology, 2025, 47(4): 932-947. doi: 10.11999/JEIT241114

Citation:

ZHAO Chuanbin, SUN Hong, ZHANG Tengyu, LUO Hongliang, WANG Yucong, JIANG Yuhua, LIN Bo, GAO Feifei. Integrated Sensing and Communications Framework for 6G: Key Technologies and Hardware Prototype Validation[J]. Journal of Electronics & Information Technology, 2025, 47(4): 932-947. doi: 10.11999/JEIT241114

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Integrated Sensing and Communications Framework for 6G: Key Technologies and Hardware Prototype Validation

doi: 10.11999/JEIT241114 cstr: 32379.14.JEIT241114

1.
Department of Automation, Tsinghua University, Beijing 100084, China
2.
China Telecom Corporation Sichuan Branch, Chengdu 610000, China
3.
China Communications Magazine Co. LTD., Beijing 100029, China

Funds: The National Natural Science Foundation of China (62325107), The Natural Science Foundation of Sichuan Province (2025YFHZ0022)

Received Date: 2024-12-19
Rev Recd Date: 2025-04-02

Available Online: 2025-04-08

Publish Date: 2025-04-01

Abstract

Abstract

Objective The Sixth-Generation (6G) mobile communications network will evolve from being human-centered to agent-centered, enabling a deep integration of multi-dimensional functions such as communication, sensing, and computation. It will further advance key physical layer technologies, including large arrays, broad bandwidth, multi-frequency bands, and multi-node collaboration, giving rise to the Integrated Sensing And Communications (ISAC) system. The ISAC system will support communication services while leveraging communication signals to sense and monitor comprehensive information about the physical world. This will empower a range of services, including low-altitude economy, digital twins, Internet of Vehicles, industrial Internet, and smart cities. However, realizing comprehensive sensing of the physical world while maintaining communication performance remains a critical challenge that requires further research. Methods This study presents a framework to deconstruct the physical world into static environments, dynamic targets, and various object materials. The static environment, which includes buildings, roads, trees, and other structures, constitutes the majority of the physical world. Sensing the static environment is fundamental for the sensory system’s understanding of the physical world. A multi-user, multi-Base Station (BS), and active-passive fusion approach for Static Environment Reconstruction (SER) is proposed. This method constructs two-dimensional or three-dimensional maps of the physical world by analyzing environmental scattering point data collected during communication between the BS and the user. Dynamic targets within the static environment, including pedestrians, vehicles, and drones, contribute to the spatiotemporal movement of the physical world. Sensing these dynamic targets is vital for enabling the sensory system to support various applications in production and daily life. A Dynamic Target Sensing (DTS) technology is proposed, leveraging multi-BS collaboration and multi-feature fusion. This technology actively transmits detection signals from the BS and receives target echo signals, enabling the monitoring of the existence, position, velocity, and category of dynamic targets. Object materials, such as metal, wood, and fabric, influence the propagation laws and interaction patterns of electromagnetic signals in the physical world. Thus, sensing the material properties of objects is crucial for the sensory system’s analysis of the fundamental laws governing the physical world. To address this, a material recognition technology based on multi-BS collaboration is proposed, which identifies the material properties of target objects by analyzing the electromagnetic coefficients of the scattering points in the BS-object-user channel. Results and Discussions Building on theoretical research, this paper presents the development of a universal ISAC hardware prototype platform based on RF System-on-Chip (RFSoC) and Field Programmable Gate Array (FPGA). With the implementation of a self-developed ISAC baseband algorithm, the platform enables real-time sensing of dynamic targets and accurate mapping of static environments. Conclusions This paper proposes a synesthesia ISAC framework based on the concept of "separation of dynamic and static," which thoroughly analyzes the interaction between the physical world and electromagnetic wave signals. It decomposes the sensing of the physical world into SER, DTS, and Object Material Recognition (OMR), thereby providing substantial support for the ultimate goal of synesthesia—accurately replicating the real physical world into a digital twin.

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References(41)

References

[1]	尹浩, 黄宇红, 韩林丛, 等. 6G通信-感知-计算融合网络的思考[J]. 中国科学: 信息科学, 2023, 53(9): 1838–1842. doi: 10.1360/SSI-2023-0135. YIN Hao, HUANG Yuhong, HAN Lincong, et al. Thoughts on 6G integrated communication, sensing and computing networks[J]. Scientia Sinica Informationis, 2023, 53(9): 1838–1842. doi: 10.1360/SSI-2023-0135.
[2]	3GPP. Study on Integrated Sensing and Communication (Release 19)[R]. TR 22.837, 2023.
[3]	LIU Wancun, ZHANG Liguo, ZHANG Xiaolin, et al. 3D snow sculpture reconstruction based on structured-light 3D vision measurement[J]. Applied Sciences, 2021, 11(8): 3324. doi: 10.3390/APP11083324.
[4]	CHOY C B, XU Danfei, GWAK J Y, et al. 3D-R2N2: A unified approach for single and multi-view 3D object reconstruction[C]. The 14th European Conference on Computer Vision, Amsterdam, The Netherlands, 2016: 628–644. doi: 10.1007/978-3-319-46484-8_38.
[5]	WANG Qiaozhi, YUAN Xiaojun, XU Chongbin, et al. A Bayesian approach to communication-driven SLAM based on diffuse reflection model[J]. IEEE Wireless Communications Letters, 2023, 12(7): 1279–1283. doi: 10.1109/LWC.2023.3271321.
[6]	LEITINGER E, GREBIEN S, LI Xuhong, et al. On the use of MPC amplitude information in radio signal based slam[C]. 2018 IEEE Statistical Signal Processing Workshop, Freiburg im Breisgau, Germany, 2018: 633–637. doi: 10.1109/SSP.2018.8450734.
[7]	YANG Jie, WEN Chaokai, XU Jing, et al. Angle-based SLAM on 5G mmWave systems: Design, implementation, and measurement[J]. IEEE Internet of Things Journal, 2023, 10(20): 17755–17771. doi: 10.1109/JIOT.2023.3279287.
[8]	QUE Hang, YANG Jie, WEN Chaokai, et al. Joint beam management and SLAM for mmWave communication systems[J]. IEEE Transactions on Communications, 2023, 71(10): 6162–6179. doi: 10.1109/TCOMM.2023.3294954.
[9]	YANG Jie, WEN Chaokai, JIN Shi, et al. Enabling plug-and-play and crowdsourcing SLAM in wireless communication systems[J]. IEEE Transactions on Wireless Communications, 2022, 21(3): 1453–1468. doi: 10.1109/TWC.2021.3104006.
[10]	BARNETO C B, RIIHONEN T, LIYANAARACHCHI S D, et al. Beamformer design and optimization for joint communication and full-duplex sensing at mm-waves[J]. IEEE Transactions on Communications, 2022, 70(12): 8298–8312. doi: 10.1109/TCOMM.2022.3218623.
[11]	WANG Xinyi, FEI Zesong, ZHANG J A, et al. Partially-connected hybrid beamforming design for integrated sensing and communication systems[J]. IEEE Transactions on Communications, 2022, 70(10): 6648–6660. doi: 10.1109/TCOMM.2022.3202215.
[12]	DU Zhen, ZHANG Zenghui, and YU Wenxian. Distributed target detection in communication interference and noise using OFDM radar[J]. IEEE Communications Letters, 2021, 25(2): 598–602. doi: 10.1109/LCOMM.2020.3026346.
[13]	WANG Shuaihu, SHEN Hong, XU Wei, et al. Clutter-aware MIMO-OFDM based target detection: Algorithm design and experimental test[C]. 2023 International Conference on Wireless Communications and Signal Processing, Hangzhou, China, 2023: 402–407. doi: 10.1109/WCSP58612.2023.10404794.
[14]	ARGYRIOU A. False target detection in OFDM-based joint RADAR-communication systems[C]. 2023 IEEE Radar Conference, San Antonio, USA, 2023: 1–6. doi: 10.1109/RadarConf2351548.2023.10149610.
[15]	STURM C and WIESBECK W. Waveform design and signal processing aspects for fusion of wireless communications and radar sensing[J]. Proceedings of the IEEE, 2011, 99(7): 1236–1259. doi: 10.1109/JPROC.2011.2131110.
[16]	CHEN Xu, FENG Zhiyong, WEI Zhiqing, et al. Code-division OFDM joint communication and sensing system for 6G machine-type communication[J]. IEEE Internet of Things Journal, 2021, 8(15): 12093–12105. doi: 10.1109/JIOT.2021.3060858.
[17]	WEI Zhiqing, QU Hanyang, JIANG Wangjun, et al. Iterative signal processing for integrated sensing and communication systems[J]. IEEE Transactions on Green Communications and Networking, 2023, 7(1): 401–412. doi: 10.1109/TGCN.2023.3234825.
[18]	CHEN Xu, FENG Zhiyong, WEI Zhiqing, et al. Multiple signal classification based joint communication and sensing system[J]. IEEE Transactions on Wireless Communications, 2023, 22(10): 6504–6517. doi: 10.1109/TWC.2023.3244195.
[19]	XIANG Yang, GAO Yuxing, YANG Xinru, et al. An ESPRIT-based moving target sensing method for MIMO-OFDM ISAC systems[J]. IEEE Communications Letters, 2023, 27(12): 3205–3209. doi: 10.1109/LCOMM.2023.3325531.
[20]	LIU Fan, YUAN Weijie, 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. doi: 10.1109/TWC.2020.3015735.
[21]	DU Zhen, LIU Fan, YUAN Weijie, et al. Integrated sensing and communications for V2I networks: Dynamic predictive beamforming for extended vehicle targets[J]. IEEE Transactions on Wireless Communications, 2023, 22(6): 3612–3627. doi: 10.1109/TWC.2022.3219890.
[22]	HAN Zixiang, DING Haiyu, ZHANG Xiaozhou, et al. Multistatic integrated sensing and communication system in cellular networks[C]. 2023 IEEE Globecom Workshops, Kuala Lumpur, Malaysia, 2023: 123–128. DOI: 10.1109/GCWkshps58843.2023.10464728.
[23]	BAUHOFER M, MANDELLI S, HENNINGER M, et al. Multi-target localization in multi-static integrated sensing and communication deployments[C]. The 2nd International Conference on 6G Networkin, Paris, France, 2023: 1–4. DOI: 10.1109/6GNet58894.2023.10317749.
[24]	GROßMANN W, HORN H, and NIGGEMANN O. Improving remote material classification ability with thermal imagery[J]. Scientific Reports, 2022, 12(1): 17288. doi: 10.1038/S41598-022-21588-4.
[25]	MILLER J L. Principles of Infrared Technology[M]. New York: Springer, 1994.
[26]	ALKHATEEB A, JIANG Shuaifeng, and CHARAN G. Real-time digital twins: Vision and research directions for 6G and beyond[J]. IEEE Communications Magazine, 2023, 61(11): 128–134. doi: 10.1109/MCOM.001.2200866.
[27]	CUI Yuanhao, YUAN Weijie, ZHANG Zhiyue, et al. On the physical layer of digital twin: An integrated sensing and communications perspective[J]. IEEE Journal on Selected Areas in Communications, 2023, 41(11): 3474–3490. doi: 10.1109/JSAC.2023.3314826.
[28]	KUMARI P, MEZGHANI A, and HEATH R W. JCR70: A low-complexity millimeter-wave proof-of-concept platform for a fully-digital SIMO joint communication-radar[J]. IEEE Open Journal of Vehicular Technology, 2021, 2: 218–234. doi: 10.1109/OJVT.2021.3069946.
[29]	LI Oupeng, HE Jia, ZENG Kun, et al. Integrated sensing and communication in 6G A prototype of high resolution THz sensing on portable device[C]. 2021 Joint European Conference on Networks and Communications & 6G Summit, Porto, Portugal, 2021: 544–549. DOI: 10.1109/EuCNC/6GSummit51104.2021.9482537.
[30]	LI Jie, YU Chao, LUO Yan, et al. Passive motion detection via mmWave communication system[C]. 2022 IEEE 95th Vehicular Technology Conference, Helsinki, Finland, 2022: 1–6. DOI: 10.1109/VTC2022-Spring54318.2022.9860809.
[31]	BARNETO C B, RASTORGUEVA-FOI E, KESKIN M F, et al. Millimeter-wave mobile sensing and environment mapping: Models, algorithms and validation[J]. IEEE Transactions on Vehicular Technology, 2022, 71(4): 3900–3916. doi: 10.1109/TVT.2022.3146003.
[32]	BARNETO C B, RIIHONEN T, TURUNEN M, et al. Radio-based sensing and indoor mapping with millimeter-wave 5G NR signals[C]. Proceedings of 2020 International Conference on Localization and GNSS, Tampere, Finland, 2020: 1–5. doi: 10.1109/ICL-GNSS49876.2020.9115568.
[33]	GUIDI F, MARIANI A, GUERRA A, et al. Indoor environment-adaptive mapping with beamsteering massive arrays[J]. IEEE Transactions on Vehicular Technology, 2018, 67(10): 10139–10143. doi: 10.1109/TVT.2018.2853657.
[34]	GUIDI F, GUERRA A, and DARDARI D. Millimeter-wave massive arrays for indoor SLAM[C]. Proceedings of 2014 IEEE International Conference on Communications Workshops, Sydney, Australia, 2014: 114–120. doi: 10.1109/ICCW.2014.6881182.
[35]	GUIDI F, GUERRA A, and DARDARI D. Personal mobile radars with millimeter-wave massive arrays for indoor mapping[J]. IEEE Transactions on Mobile Computing, 2016, 15(6): 1471–1484. doi: 10.1109/TMC.2015.2467373.
[36]	GUIDI F, GUERRA A, DARDARI D, et al. Environment mapping with millimeter-wave massive arrays: System design and performance[C]. Proceedings of 2016 IEEE Globecom Workshops, Washington, USA, 2016: 1–6. DOI: 10.1109/GLOCOMW.2016.7848895.
[37]	LOTTI M, PASOLINI G, GUERRA A, et al. Radio SLAM for 6G systems at THz frequencies: Design and experimental validation[J]. IEEE Journal of Selected Topics in Signal Processing, 2023, 17(4): 834–849. doi: 10.1109/JSTSP.2023.3285101.
[38]	YIN Mingsheng, VELDANDA A K, TRIVEDI A, et al. Millimeter wave wireless assisted robot navigation with link state classification[J]. IEEE Open Journal of the Communications Society, 2022, 3: 493–507. doi: 10.1109/OJCOMS.2022.3155572.
[39]	MOU Zhiyu and GAO Feifei. Millimeter wave wireless communication assisted three-dimensional simultaneous localization and mapping[J]. arXiv: 2303.02617, 2023.
[40]	PALACIOS J, BIELSA G, CASARI P, et al. Communication-driven localization and mapping for millimeter wave networks[C]. Proceedings of the IEEE INFOCOM 2018 - IEEE Conference on Computer Communications, Honolulu, USA, 2018: 2402–2410. doi: 10.1109/INFOCOM.2018.8485819.
[41]	PALACIOS J, CASARI P, and WIDMER J. JADE: Zero-knowledge device localization and environment mapping for millimeter wave systems[C]. Proceedings of the IEEE INFOCOM 2017 - IEEE Conference on Computer Communications, Atlanta, USA, 2017: 1–9. doi: 10.1109/INFOCOM.2017.8057183.