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随着雷达、电子战和5G通信等无线射频技术的快速发展, 对宽带射频信号的测量和实时频谱表征变得越来越重要. 传统射频信号实时测量技术受模数转换器采样率和数字信号处理能力的限制, 存在测量带宽窄、数据量大、易受电磁干扰等问题. 本文提出一种基于量子压缩感知的射频信号测量技术, 使用集成电光晶体作为射频传感, 通过被测射频信号调制光子波函数构建压缩感知机, 实现对宽带射频信号的压缩测量, 显著提升了频谱感知带宽. 实验演示了工频和中频高压信号的长时间频谱监测, 以及高频射频信号的实时频谱测量. 在傅里叶极限频谱分辨率下, 实现了GHz量级的实时频谱分析带宽, 数据压缩率达到1.7×10–5, 可以满足5G无线通信、认知无线电等应用对宽带射频信号频谱测量的需求, 为发展下一代宽带频谱感知技术提供了新的技术路径.With the rapid development of radio frequency technology such as radar, electronic warfare and 5G communication, the measurement and real-time spectrum characterization of broadband radio frequency signals become increasingly important. The traditional radio frequency signal real-time measurement technology is limited by the sampling rate of analog-to-digital converter and the ability to process digital signals, and encounters the problems of narrow measurement band, large data volume, and susceptibility to electromagnetic interference. This work is to study a radio frequency signal measurement technology based on quantum compression sensing, which uses integrated electro-optical crystal as radio frequency sensor, and constructs a compression sensing machine by modulating the photon wave function of the measured radio frequency signal to realize the compression measurement of broadband radio frequency signal, significantly improving the spectrum sensing bandwidth. The experiment demonstrates the long-term spectrum monitoring of power frequency and intermediate frequency high voltage signals, and the real-time spectrum measurement of high frequency radio frequency signals. Under the Fourier limit spectrum resolution, the real-time spectrum analysis bandwidth of GHz magnitude is realized, and the data compression rate reaches 1.7×10–5, which can meet the needs of 5G wireless communication, cognitive radio and other applications for broadband radio frequency signal spectrum measurement, and provide a new technical path for developing the next-generation broadband spectrum sensing technology.
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Keywords:
- broadband microwave spectrum measurement /
- quantum compressive sensing /
- radio frequency sensing /
- real-time analysis bandwidth
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图 1 射频信号传感结构示意图
Fig. 1. Schematic diagram of radio frequency signal sensing structure.
图 2 基于量子压缩感知的宽带射频信号测量系统示意图
Fig. 2. Schematic diagram of broadband radio frequency signal measurement system based on quantum compressed sensing.
图 3 工频电场作用下射频信号传感响应
Fig. 3. Sensor response under the action of power frequency electric field.
图 4 射频信号传感特性 (a) 输入输出振幅特性; (b) 频率响应曲线
Fig. 4. Radio frequency signal sensing characteristics: (a) Input and output characteristics; (b) frequency response curve.
图 5 施加电场波形及射频信号传感响应 (a) 50 Hz正弦波电场响应; (b) 50 Hz三角波电场响应; (c) 50 Hz方波电场响应; (d) 1 kHz正弦波电场响应
Fig. 5. Applied electric field waveform and radio frequency signal sensing response: (a) 50 Hz sine wave electric field response; (b) 50 Hz triangle wave electric field response; (c) 50 Hz square wave electric field response; (d) 1 kHz sine wave electric field response.
图 6 短时低频频谱感知 (a) 工频频谱感知结果; (b) 中频频谱感知结果
Fig. 6. Short-term low-frequency spectrum perception: (a) Power frequency spectrum perception results; (b) intermediate frequency spectrum perception results.
图 7 长时监测频谱感知 (a)工频频谱测量结果; (b)中频频谱测量结果
Fig. 7. Long-term monitoring spectrum perception: (a) Power frequency spectrum perception results; (b) intermediate frequency spectrum perception results.
图 8 短时频谱感知线宽分析 (a) 工频频谱线宽; (b) 中频频谱线宽
Fig. 8. Linewidth analysis of short-time spectrum sensing: (a) Power frequency spectrum linewidth; (b) intermediate frequency spectrum linewidth.
图 9 长时监测频谱感知线宽分析 (a) 工频频谱线宽; (b) 中频频谱线宽
Fig. 9. Long-term monitoring spectrum perception linewidth analysis: (a) Power frequency spectrum linewidth; (b) intermediate frequency spectrum linewidth.
图 10 宽带信号频谱恢复结果 (a) 1 GHz信号频谱; (b) 2 GHz信号频谱
Fig. 10. Broadband signal spectrum recovery result: (a) 1 GHz signal spectrum; (b) 2 GHz signal spectrum.
表 1 线性拟合分析结果
Table 1. Linear fitting analysis results.
截距C 斜率K 线性拟合度R 参数值/mV 标准差 参数值/mV 标准差 0.99611 3.41944 0.98036 0.73717 0.01742 
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[1] Lu H H, Li C Y, Tsai W S, Lin R D, Tang Y S, Chen Y X, Lin Y S, Fan W C 2022 J. Lightw. Technol. 40 7790
Google Scholar
[2] Qi Y H, Yang G, Liu L, Fan J, Antonio O, Kong H W, Yu W, Yang Z P 2017 IEEE Trans. Electromagn. Compat. 59 1661
Google Scholar
[3] William B, Elias A, Aboulnasr H 2022 IEEE Trans. Signal Process. 70 729
Google Scholar
[4] Gupta M S, Kumar K 2019 J. Netw. Comput. Appl. 143 47
Google Scholar
[5] Chen L, Liu Y 2016 International Conference on Information Science and Control Engineering Beijing, China, July 8–10, 2016 p1379
[6] Shi J Z, Zhang F Z, Ben D, Pan S L 2020 J. Lightw. Technol. 38 2171
Google Scholar
[7] Zou X H, Lu B, Pan W, Yan L S, Stohr A, Yao J P 2016 Laser Photonics Rev. 10 711
Google Scholar
[8] Murat T, Won N 2021 IEEE Access 9 30060
Google Scholar
[9] David L D 2006 IEEE Trans. Inf. Theory 52 1289
Google Scholar
[10] Candes E J, Romberg J 2006 Found. Comput. Math. 6 227
Google Scholar
[11] Candes E J, Tao T 2006 IEEE Trans. Inf. Theory 52 5406
Google Scholar
[12] Ragheb T, Kirolos S, Laska J, Gilbert A, Strauss M, Baraniuk R, Massoud Y 2007 50th Midwest Symposium on Circuits and Systems Montreal QC, Canada, August 5–8, 2007 p325
[13] Qin Z J, Fan J C, Liu Y W, Gao Y, Li G Y 2018 IEEE Signal Processing Mag. 35 40
Google Scholar
[14] Shin H, Harjani R 2017 IEEE J. Solid-State Circuits 52 1753
Google Scholar
[15] Shindo D, Tanigaki T, Park H S 2017 Adv. Mater. 29 1602216
Google Scholar
[16] Zhang J Y, Li X Z, Du C H, Jiang Y, Ma Z G, Chen H, Jia H Q, Wang W X, Deng Z 2022 IEEE Photon. J. 14 1
Google Scholar
[17] 方云团, 王誉雅, 夏景 2019 68 194201
Google Scholar
Fang Y T, Wang Y Y, Xia J 2019 Acta Phys. Sin. 68 194201
Google Scholar
[18] Seng F, Stan N, King R, Josephson C, Shumway L, Hammond A, Velasco I, Johnston H, Schultz S M 2017 J. Lightw. Technol. 35 669
Google Scholar
[19] Li Y S, Gao L, Wan J, Liu J 2020 Appl. Opt. 59 6237
Google Scholar
[20] Zhu B B, Xue M, Yu C Y, Pan S L 2021 Chin. Opt. Lett. 19 101202
Google Scholar
[21] Shi D F, Li G Y, Jia Z Y, Wen J, Li M, Zhu N H, Li W 2021 Opt. Eepress 29 19515
Google Scholar
[22] Luo M D, Yang F, Dong F N, Chen N, Liao W 2022 J. Lightw. Technol. 40 2577
Google Scholar
[23] Wang X X, Korzh B A, Weigel P O, Nemchick D J, Drouin B J, Becker W, Zhao Q Y, Zhu D, Colangelo M, Dane A E, Berggren K K, Shaw M D, Mookherjea S 2019 J. Lightw. Technol. 38 166
[24] Hao T F, Yang Y, Jin Y Q, Xiang X, Li W, Zhu N H, Dong R F, Li M 2022 J. Lightw. Technol. 40 6616
Google Scholar
[25] Zhu L, Wang G J, Huang F M, Li Y, Chen W, Hong H Y 2022 IEEE Geosci. Remote Sens. Lett. 19 1
Google Scholar
[26] Baraniuk R G 2007 IEEE Signal Processing Mag. 24 118
Google Scholar
[27] Hu J Y, Yu B, Jing M Y, Xiao L T, Jia S T, Qin G Q, Long G L 2016 Light Sci. Appl. 5 16144
Google Scholar
[28] Hu J Y, Jing M Y, Zhang G F, Qin C B, Xiao L T, Jia S T 2018 Opt. Express 26 20835
Google Scholar
[29] Hu J Y, Liu Y, Liu L L, Yu B, Zhang G F, Xiao L T, Jia S T 2015 Photon. Res. 3 24
Google Scholar
[30] 李长胜 2014 63 074207
Google Scholar
Li C S 2014 Acta Phys. Sin. 63 074207
Google Scholar
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