基于空中计算架构的通信感知计算一体化

doi:10.19665/j.issn1001-2400.2023.03.003

西安电子科技大学学报 ›› 2023, Vol. 50 ›› Issue (3): 31-39.doi: 10.19665/j.issn1001-2400.2023.03.003

• 面向IT3.0的感通算融合6G关键技术专题 • 上一篇下一篇

基于空中计算架构的通信感知计算一体化

韩凯峰¹(),周梓钦²(),王志勤¹(),贡毅²(),李晓阳³()

1.中国信息通信研究院移动通信创新中心,北京 100083
2.南方科技大学电子与电气工程系,广东深圳 518055
3.深圳市大数据研究院,广东深圳 518115

收稿日期:2022-11-22 出版日期:2023-06-20 发布日期:2023-10-13
作者简介:韩凯峰(1993—),男,高级工程师,E-mail:hankaifeng@caict.ac.cn；|周梓钦(1998—),男,南方科技大学硕士研究生,E-mail:12032875@mail.sustech.edu.cn；|王志勤(1970—),女,教授级高级工程师,E-mail:zhiqin.wang@caict.ac.cn；|贡毅(1973—),男,教授,E-mail:gongy@sustech.edu.cn
基金资助:
中国科协青年人才托举工程项目(2022QNRC001);国家重点研发计划(2021YFB2900200);国家自然科学基金(62071212);国家自然科学基金(62101235);广东省基础与应用基础研究基金(2019B1515130003);深圳市自然科学基金(JCYJ20220530113017039)

Integrated sensing,communication,and computation via the over-the-air computing architecture

HAN Kaifeng¹(),ZHOU Ziqin²(),WANG Zhiqin¹(),GONG Yi²(),LI Xiaoyang³()

1. Mobile Communications Innovation Center,China Academy of Information and Communication Technology,Beijing 100083,China
2. Department of Electronic and Electrical Engineering,Southern University of Science and Technology,Shenzhen 518055,China
3. Shenzhen ResearchInstitute of Big Data,Shenzhen 518115,China

Received:2022-11-22 Online:2023-06-20 Published:2023-10-13

摘要/Abstract

摘要：

受限于设备条件和计算机网络层级,传统研究将传感设备端的数据感知、服务器端的数据计算和两端之间的数据通信视为相互独立的环节,缺乏全局考虑,严重阻碍了数据处理的效率。为了提高数据感知和传输效率,通信感知融合技术致力于设计同时支持雷达感知和数据通信的复用信号。为了提高数据传输与计算效率,空中计算旨在利用信号在无线多址信道传输过程中的波形叠加性质,在数据传输的同时直接进行计算。为了实现高效准确的数据处理,可以利用通信感知融合技术中的复用信号和空中计算技术中的波形叠加属性,实现基于空中计算架构的通信感知计算一体化,并通过对波束赋形器等信号处理环节进行设计,以降低信道噪声和信号干扰,从而提升数据在通信感知计算过程中的准确度。该技术可应用在目标探测、车联网、边缘智能等多个领域。实验结果表明,相比于传统方案,基于空中计算架构的通信感知计算一体化可以显著提升数据处理的效率和准确度。

关键词: 通信感知计算一体化, 通信感知融合, 无线空中计算架构

Abstract:

Limited by equipment conditions and computer network hierarchy,traditional researches treat data sensing,communication,and computation as independent processes,which lack global consideration and seriously hinder the efficiency of data processing.To improve the efficiencies of data sensing and transmission,the sensing-communication fusion is proposed to design dual-functional signals for supporting both radar sensing and data communication.To improve the efficiencies of data communication and computation,over-the-air computing aims to exploit the waveform superposition property of signals in the multiple access channel to enable data computation during transmission.In order to achieve efficient data processing,the dual-functional signals in sensing-communication fusion and the waveform superposition property in over-the-air computing are utilized to realize the integrated sensing,communication,and computation over the air.The corresponding beamformers are designed to reduce channel noise and signal interference,so as to improve the accuracies of sensing,communication,and computation.This technology can be applied in various fields such as target detection,vehicle networking,and edge intelligence.Experimental results show that the technology can significantly improve the efficiency and accuracy of data processing in comparison with the traditional schemes.

Key words: integrated sensing, communication, and computation, sensing-communication fusion, over-the-aircomputingarchitecture

中图分类号:

TN929.5

韩凯峰,周梓钦,王志勤,贡毅,李晓阳. 基于空中计算架构的通信感知计算一体化[J]. 西安电子科技大学学报, 2023, 50(3): 31-39.

HAN Kaifeng,ZHOU Ziqin,WANG Zhiqin,GONG Yi,LI Xiaoyang. Integrated sensing,communication,and computation via the over-the-air computing architecture[J]. Journal of Xidian University, 2023, 50(3): 31-39.

图/表 7

图1

图2

图3

图4

图5

图6

图7

参考文献 18

[1]	SAAD W, BENNIS M, CHEN M. AVision of 6G Wireless Systems:Applications,Trends,Technologies,and Open Research Problems[J]. IEEE Network, 2019, 34(3):134-142.
[2]	IMT-2030(6G)推进组. 6G典型场景和关键能力[R]. 北京: IMT- 2030(6G)推进组,2022.
[3]	LI X, LIU F, ZHOU Z, et al. IntegratedSensing and Over-the-Air Computation:Dual-Functional MIMO Beamforming Design[C]// Proceedings of IEEE International Conference on 6G Networking (6GNet).Piscataway:IEEE, 2022:1-8.
[4]	MAO Y, YOU C, ZHANG J, et al. A Survey on Mobile Edge Computing:The Communication Perspective[J]. IEEE Communications Surveys & Tutorials, 2017, 19(4):2322-2358.
[5]	WANG Z, HAN K, JIANG J, et al. Symbiotic Sensing and Communications Towards 6G:Vision,Applications,and Technology Trends[C]// Proceedings of IEEE 94th Vehicular Technology Conference (VTC2021-Fall).Piscataway:IEEE, 2021:1-5.
[6]	LI X, YOU C, ANDREEV S, et al, Wirelessly Powered Crowd Sensing:Joint Power Transfer,Sensing,Compression,and Transmission[J]. IEEE Journal on Selected Areas in Communications, 2018, 37(2):391-406. doi: 10.1109/JSAC.2018.2872379
[7]	CUI Y, LIU F, JING X, et al. Integrating Sensing and Communications for Ubiquitous IoT:Applications,Trends,and Challenges[J]. IEEE Network, 2021, 35(5):158-167. doi: 10.1109/MNET.010.2100152
[8]	LIU F, MASOUROS C, PETROPULU A P, et al. Joint Radar and Communication Design:Applications,State-of-the-Art,and the Road Ahead[J]. IEEE Transactions on Communications, 2020, 68(6):3834-3862. doi: 10.1109/TCOMM.26
[9]	MEALEY R M. A Method for Calculating Error Probabilities in a Radar Communication System[J]. IEEE Transactions on Space Electronics and Telemetry, 1963, 9(2):37-42. doi: 10.1109/TSET.1963.4337601
[10]	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, 2015, 64(2):464-474. doi: 10.1109/TSP.2015.2483485
[11]	BLUNT S D, METCALF J G, BIGGS C R, et al. Performance Characteristics and Metrics for Intra-Pulse Radar Embedded Communication[J]. IEEE Journal on Selected Areas in Communications, 2011, 29(10):2057-2066. doi: 10.1109/JSAC.2011.111215
[12]	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, 2015, 64(8):2168-2181. doi: 10.1109/TSP.2015.2505667
[13]	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. doi: 10.1109/TWC.2018.2803045
[14]	CHEN L, ZHAO N, CHEN Y, et al. Over-the-Air Computation for IoT Networks:Computing Multiple Functions with Antenna Arrays[J]. IEEE Internet of Things Journal, 2018, 5(6):5296-5306. doi: 10.1109/JIOT.2018.2843321
[15]	NAZER B, GASTPAR M. Computation Over Multiple-Access Channels[J]. IEEE Transactions on Information Theory, 2017, 53(10):3498-3516. doi: 10.1109/TIT.2007.904785
[16]	LI X, ZHU G, GONG Y, et al. Wirelessly Powered Data Aggregation for IoT via Over-the-Air Function Computation:Beamforming and Power Control[J]. IEEE Transactions on Wireless Communications, 2019, 18(7):3437-3452. doi: 10.1109/TWC.7693
[17]	BEKKERMAN I, TABRIKIAN J. Target Detection and Localization Using MIMO Radars and Sonars[J]. IEEE Transactions on Signal Processing, 2006, 54(10):3873-3883. doi: 10.1109/TSP.2006.879267
[18]	STURM C, 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

基于空中计算架构的通信感知计算一体化

Integrated sensing,communication,and computation via the over-the-air computing architecture

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 7

参考文献 18

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	鲜永菊, 赵润昊, 邢智童, 李云. 面向通感一体OFDM系统峰均比抑制的研究[J]. 西安电子科技大学学报, 2024, 51(6): 40-51.
[2]	滕晓坤, 孟声国, 陈伟聪, 唐万恺, 金石. 智能超表面毫米波覆盖增强波束设计与测量[J]. 西安电子科技大学学报, 2024, 51(5): 189-200.
[3]	朱思峰, 宋兆威, 陈昊, 朱海, 乔蕊. 智慧交通场景下云边端协同的多目标优化卸载决策[J]. 西安电子科技大学学报, 2024, 51(3): 63-75.
[4]	王雨来, 廖晓闽, 何海光, 叶国军. 知识图谱辅助的无人机群频谱资源优化算法[J]. 西安电子科技大学学报, 2024, 51(5): 58-70.
[5]	孙彦景, 李林, 王博文, 李松. 灾后无人机自组网高动态多信道TDMA调度算法[J]. 西安电子科技大学学报, 2024, 51(2): 56-67.
[6]	姜来为, 陈正, 杨宏宇. 高动态复杂场景下航空自组网路由协议研究[J]. 西安电子科技大学学报, 2024, 51(1): 72-85.
[7]	廖晓闽, 韩双利, 朱璇, 林初善, 王海鹏. 无人机干扰辅助认知隐蔽通信资源优化算法[J]. 西安电子科技大学学报, 2023, 50(6): 75-83.
[8]	吴义豪,齐彦丽,周一青,蔡青,刘玲,石晶林. 通感算协同的无人机群轨迹规划与功率分配[J]. 西安电子科技大学学报, 2023, 50(3): 61-74.
[9]	黄志民, 肖琨. 能量收集分布式基站系统的下行射频远端选择[J]. 西安电子科技大学学报, 2023, 50(2): 71-80.
[10]	李钊,胡娇娇. 一种采用信号虚拟分解的多址接入方法[J]. 西安电子科技大学学报, 2023, 50(1): 12-18.
[11]	刘成玉, 张碧桂, 李钊. 一种采用松弛正交预编码的多址接入方法[J]. 西安电子科技大学学报, 2023, 50(1): 29-35.
[12]	张航宇, 张锐, 廖方圆, 李勇朝. 基于交叉熵的1位DAC大规模MIMO预编码方案[J]. 西安电子科技大学学报, 2022, 49(6): 1-8.
[13]	齐佩汉,李冰,谢爱平,高向兰. 欠采样跳频通信信号深度学习重构方法[J]. 西安电子科技大学学报, 2022, 49(4): 1-7.
[14]	刘刚,娄增进,林勤华,郭漪. 大规模MIMO系统中的改进CG迭代算法[J]. 西安电子科技大学学报, 2022, 49(4): 8-15.
[15]	王汝言,刘佳,何鹏,崔亚平. 偏好感知的边云协同群智感知参与者选择策略[J]. 西安电子科技大学学报, 2022, 49(1): 142-151.