Advances and Challenges in Wireless Channel Hardware Twin

FANG Sheng; ZHU Qiuming; XIE Yuetian; JIANG Hao; LI Hui; WU Qihui; MAO Kai; HUA Boyu

doi:10.11999/JEIT241093

Volume 47 Issue 8

Aug. 2025

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(8): 2416-2428

FANG Sheng, ZHU Qiuming, XIE Yuetian, JIANG Hao, LI Hui, WU Qihui, MAO Kai, HUA Boyu. Advances and Challenges in Wireless Channel Hardware Twin[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2416-2428. doi: 10.11999/JEIT241093

Citation:

FANG Sheng, ZHU Qiuming, XIE Yuetian, JIANG Hao, LI Hui, WU Qihui, MAO Kai, HUA Boyu. Advances and Challenges in Wireless Channel Hardware Twin[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2416-2428. doi: 10.11999/JEIT241093

Citation:

PDF( 1738 KB)

Advances and Challenges in Wireless Channel Hardware Twin

doi: 10.11999/JEIT241093 cstr: 32379.14.JEIT241093

1.
College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2.
National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China

Funds: The National Natural Science Foundation of China (62271250, U23B2005)

Received Date: 2024-12-10
Rev Recd Date: 2025-07-17

Available Online: 2025-07-24

Publish Date: 2025-08-27

Abstract

Abstract

Significance Wireless channel characteristics are key determinants of communication system performance and reliability. Channel twin technology—defined as the physical or digital reproduction of channel distortion effects—has become essential for system testing and validation. Digital twin approaches are favored for their flexibility and efficiency, and hardware-based platforms (i.e., channel emulators, CEs) are widely used for large-scale performance evaluation. However, as networks advance toward Terahertz (THz) bands, extremely large-scale massive Multiple-Input Multiple-Output (XL-MIMO) systems (e.g., 1024-antenna arrays), and integrated air-space-ground-sea communications, three key limitations remain: (1) Inability to support real-time processing for ultra-wide bandwidths over 10 GHz; (2) Inadequate dynamic emulation accuracy for non-stationary channels under high mobility; and (3) Insufficient hardware resources for simulating over 10⁶ concurrent channels in XL-MIMO architectures. This study reviews state-of-the-art hardware twin technologies, identifies critical technical bottlenecks, and outlines future directions for next-generation channel emulation platforms. Progress Existing channel hardware twin technologies can be categorized into three paradigms based on channel data sources, model types, and implementation architectures. First, measured data-driven twin technology uses real-world propagation data to enable high-fidelity emulation. Signal replay reproduces specific electromagnetic environments by replaying recorded signals. Although this approach preserves scene-specific authenticity, it lacks flexibility due to dependence on measured datasets and storage constraints. Channel Impulse Response (CIR) replay extracts propagation characteristics from measurement data, making it applicable to unmodeled environments such as underwater acoustics. However, its accuracy depends on precise channel estimation and is limited by data sampling resolution and storage capacity. Second, deterministic model-driven twin technology generates CIR using Finite Impulse Response (FIR) filters by synthesizing multipath delays and fading coefficients for predefined scenarios. Techniques such as sparse filtering and subspace projection optimize the trade-off between accuracy and hardware efficiency. For example, current large-scale emulators support 256×256 MIMO systems with 512-tap FIR filters, requiring only four active taps. Nonetheless, limited clock resolution introduces phase distortion in the frequency response, reducing fidelity in high-frequency terahertz applications. Third, statistical model-driven twin technology emulates time-varying channel behavior by generating fading and delay profiles based on probability distributions. The sum-of-sinusoids method is widely employed due to its simplicity and low computational demand, while enhanced implementations—such as the coordinate rotating digital computer algorithm—minimize storage requirements for sinusoid generation. This paradigm offers strong scalability but sacrifices scenario-specific fidelity, limiting its ability to reproduce certain channel characteristics accurately. A comparative analysis across fidelity, flexibility, scalability, and implementation complexity shows that measured data-driven methods are best suited for reproducing real-world environments; deterministic models support configurable scenario design for known settings; and statistical models facilitate efficient emulation of large-scale networks. Each approach balances distinct advantages against inherent limitations. Prospects Future developments in channel hardware twin technologies are expected to integrate emerging innovations to overcome current limitations: (1) Deep learning techniques—such as generative adversarial networks—can learn from limited measured channel data to synthesize channel characteristics, reducing dependence on extensive datasets in measured data-driven approaches. (2) The environment-aware capabilities of next-generation communication networks enable dynamic reconstruction of propagation environments, addressing the lack of real-time adaptability in deterministic model-driven technologies. (3) Transfer learning enables the migration of knowledge across propagation scenarios, improving the cross-scenario generalization of statistical model-driven emulation without requiring large amounts of measured data. Future applications of channel hardware twin technologies are expected to advance in three primary directions: (1) real-time optimization of communication systems; (2) network planning and design; and (3) testing and evaluation of electromagnetic devices. Through the integration of deep learning and environmental sensing, hardware twin platforms will support intelligent, self-adaptive communication systems capable of meeting the increasing complexity of future network demands. Conclusions This review synthesizes recent progress in channel hardware twin technologies and addresses critical challenges posed by future communication scenarios characterized by ultra-wide bandwidths, high channel dynamics, and large-scale networking. Key issues include high-frequency wideband signal processing, emulation of non-stationary dynamic environments, and scalability to large multi-branch network architectures. A classification framework is proposed, categorizing existing hardware twin approaches into three paradigms—measured data-driven, deterministic model-driven, and statistical model-driven—based on data sources, modeling strategies, and implementation architectures. A comparative analysis of these paradigms evaluates their relative strengths and limitations in terms of authenticity, flexibility, scalability, and emulation duration, providing guidance for selecting appropriate emulation strategies in complex environments. Furthermore, this study explores the integration of emerging technologies such as generative networks, environmental sensing, and transfer learning to support data-efficient generation, dynamic scenario adaptation, and cross-scene generalization. These advancements are expected to enhance the efficiency and adaptability of channel hardware twins, enabling them to meet the stringent requirements of future communication systems in performance validation, network design, and device testing. This work offers a foundation for advancing innovation in channel hardware twin technologies and accelerating the development of next-generation wireless networks.
- Wireless channel,
- Digital twin,
- Channel hardware twin,
- Channel characteristics,
- Communication performance testing

FullText(HTML)

References(95)

References

[1]	LIU Ting, GUAN Ke, HE Danping, et al. 6G integrated sensing and communications channel modeling: Challenges and opportunities[J]. IEEE Vehicular Technology Magazine, 2024, 19(2): 31–40. doi: 10.1109/MVT.2024.3373930.
[2]	WANG Heng, ZHANG Jianhua, NIE Gaofeng, et al. Digital twin channel for 6G: Concepts, architectures and potential applications[J]. IEEE Communications Magazine, 2025, 63(3): 24–30. doi: 10.1109/MCOM.001.2400213.
[3]	LI Junling, WANG Chengxiang, HUANG Chen, et al. Digital twin online channel modeling: Challenges, principles, and applications[J]. IEEE Vehicular Technology Magazine, 2025, 20(1): 94–103. doi: 10.1109/MVT.2025.3527729.
[4]	KIHERO A B, KARABACAK M, and ARSLAN H. Emulation techniques for small scale fading aspects by using reverberation chamber[J]. IEEE Transactions on Antennas and Propagation, 2019, 67(2): 1246–1258. doi: 10.1109/TAP.2018.2883571.
[5]	HATA M and NAGATSU T. Mobile location using signal strength measurements in a cellular system[J]. IEEE Transactions on Vehicular Technology, 1980, 29(2): 245–252. doi: 10.1109/T-VT.1980.23848.
[6]	FAILLI M. Digital land mobile radio communications[EB/OL]. https://op.europa.eu/en/publication-detail/-/publication/61fc77e7-bca2-4229-8eb4-77741f0d2ab2, 1989.
[7]	PODDAR H, JU Shihao, SHAKYA D, et al. A tutorial on NYUSIM: Sub-terahertz and millimeter-wave channel simulator for 5G, 6G, and beyond[J]. IEEE Communications Surveys & Tutorials, 2024, 26(2): 824–857. doi: 10.1109/COMST.2023.3344671.
[8]	FRAUNHOFER HHI. QuaDRiGa channel model[EB/OL]. https://quadriga-channel-model.de, 2023.
[9]	TARBOUSH S, SARIEDDEEN H, CHEN Hui, et al. TeraMIMO: A channel simulator for wideband ultra-massive MIMO terahertz communications[J]. IEEE Transactions on Vehicular Technology, 2021, 70(12): 12325–12341. doi: 10.1109/TVT.2021.3123131.
[10]	BELZARENA P. PyWiCh: Python wireless channel simulator[C]. 2022 IEEE Latin-American Conference on Communications, Rio de Janeiro, Brazil, 2022: 1–6. doi: 10.1109/LATINCOM56090.2022.10000470.
[11]	ALKHATEEB A. DeepMIMO: A generic deep learning dataset for millimeter wave and massive MIMO applications[EB/OL]. https://arxiv.org/abs/1902.06435, 2019.
[12]	BUPT-ARTT Lab, IMT-2030 THz channel model platform[EB/OL]. http://www.zjhlab.net/publications/buptcmg-imt2030_thz-channel-model-platform, 2023.
[13]	BJTU APC Lab, CloudRT ray-tracing simulation platform[EB/OL]. http://www.raytracer.cloud/, 2021.
[14]	WANG Chengxiang, LV Zhen, CHEN Yunfei, et al. A complete study of space-time-frequency statistical properties of the 6G pervasive channel model[J]. IEEE Transactions on Communications, 2023, 71(12): 7273–7287. doi: 10.1109/TCOMM.2023.3307144.
[15]	BONATI L, JOHARI P, POLESE M, et al. Colosseum: Large-scale wireless experimentation through hardware-in-the-loop network emulation[C]. 2021 IEEE International Symposium on Dynamic Spectrum Access Networks, Los Angeles, USA, 2021: 105–113. doi: 10.1109/DySPAN53946.2021.9677430.
[16]	DAKIĆ A, RAINER B, HOFER M, et al. Hardware-in-the-loop framework for testing wireless V2X communication[C]. 2023 IEEE Wireless Communications and Networking Conference, Glasgow, UK, 2023: 1–6. doi: 10.1109/WCNC55385.2023.10118673.
[17]	ZHU Qiuming, ZHAO Zikun, MAO Kai, et al. A real-time hardware emulator for 3D non-stationary U2V channels[J]. IEEE Transactions on Circuits and Systems I: Regular Papers, 2021, 68(9): 3951–3964. doi: 10.1109/TCSI.2021.3087777.
[18]	KEYSIGHT Technologies. F8800A PROPSIM F64 channel emulator[EB/OL]. https://www.keysight.com/us/en/product/F8800A, 2023.
[19]	坤恒顺维. KSW-WNS04无线信道仿真仪[EB/OL]. https://www.ksw-tech.com/products/ksw-wns04-wireless-channel-simulator.html, 2024. KSW Technologies Co. ,Ltd. KSW-WNS04 Wireless Channel Simulator[EB/OL]. https://www.ksw-tech.com/products/ksw-wns04-wireless-channel-simulator.html, 2024.
[20]	Spirent Communications. Spirent vertex channel emulator[EB/OL]. https://www.spirent.com/assets/u/spirent_vertex_channel_emulator_datasheet, 2024.
[21]	ROHDE&SCHWARZ. R&S®SMW200A vector signal generator[EB/OL]. https://www.rohde-schwarz.com/us/products/test-and-measurement/vector-signal-generators/rs-smw200a-vector-signal-generator_63493-38656.html, 2024.
[22]	JI Yilin and FAN Wei. Enabling high-fidelity ultra-wideband radio channel emulation: Band-stitching and digital predistortion concepts[J]. IEEE Open Journal of Antennas and Propagation, 2022, 3: 932–939. doi: 10.1109/OJAP.2022.3198287.
[23]	GHOSH A and KIM M. THz channel sounding and modeling techniques: An overview[J]. IEEE Access, 2023, 11: 17823–17856. doi: 10.1109/ACCESS.2023.3246161.
[24]	FAN Wei, KYÖSTI P, HENTILÄ L, et al. A flexible millimeter-wave radio channel emulator design with experimental validations[J]. IEEE Transactions on Antennas and Propagation, 2018, 66(11): 6446–6451. doi: 10.1109/TAP.2018.2864339.
[25]	CAO Jue, TILA F, and NIX A. Design and implementation of a wideband channel emulation platform for 5G mmWave vehicular communication[J]. IET Communications, 2020, 14(14): 2369–2376. doi: 10.1049/iet-com.2019.1016.
[26]	JI Yilin, FAN Wei, and PEDERSEN G. Wideband radio channel emulation using band-stitching schemes[C]. 2020 14th European Conference on Antennas and Propagation, Copenhagen, Denmark, 2020: 1–5. doi: 10.23919/EuCAP48036.2020.9135788.
[27]	ZHANG Fengchun, BENGTSON M F, KYÖSTI P, et al. Dynamic sub-THZ radio channel emulation: Principle, challenges, and experimental validation[J]. IEEE Wireless Communications, 2024, 31(1): 10–16. doi: 10.1109/MWC.001.2300286.
[28]	FENG Ruirui, MAO Kai, ZHU Qiuming, et al. Real-time hardware emulation of frequency non-stationary UWB channels with continuous frequency response[C]. 2022 IEEE 22nd International Conference on Communication Technology, Nanjing, China, 2022: 999–1003. doi: 10.1109/ICCT56141.2022.10072872.
[29]	朱秋明, 倪浩然, 华博宇, 等. 无人机毫米波信道测量与建模研究综述[J]. 移动通信, 2022, 46(12): 1–11. doi: 10.3969/j.issn.1006-1010.20221114-0001. ZHU Qiuming, NI Haoran, HUA Boyu, et al. A survey of UAV millimeter-wave channel measurement and modeling[J]. Mobile Communications, 2022, 46(12): 1–11. doi: 10.3969/j.issn.1006-1010.20221114-0001.
[30]	张在琛, 江浩. 智能超表面使能无人机高能效通信信道建模与传输机理分析[J]. 电子学报, 2023, 51(10): 2623–2634. doi: 10.12263/DZXB.20221352. ZHANG Zaichen and JIANG Hao. Channel modeling and characteristics analysis for high energy-efficient RIS-Assisted UAV communications[J]. Acta Electronica Sinica, 2023, 51(10): 2623–2634. doi: 10.12263/DZXB.20221352.
[31]	MAO Kai, ZHU Qiuming, WANG Chengxiang, et al. A survey on channel sounding technologies and measurements for UAV-assisted communications[J]. IEEE Transactions on Instrumentation and Measurement, 2024, 73: 8004624. doi: 10.1109/TIM.2024.3436128.
[32]	HOFER M, XU Zhinan, VLASTARAS D, et al. Validation of a real-time geometry-based stochastic channel model for vehicular scenarios[C]. 2018 IEEE 87th Vehicular Technology Conference, Porto, Portugal, 2018: 1–5. doi: 10.1109/VTCSpring.2018.8417476.
[33]	ZHU Qiuming, LIU Xinglin, YIN Xuefeng, et al. A novel simulator of nonstationary random MIMO channels in Rayleigh fading scenarios[J]. International Journal of Antennas and Propagation, 2016, 2016: 3492591. doi: 10.1155/2016/3492591.
[34]	李浩, 朱秋明, 陈应兵, 等. 非平稳信道衰落FPGA实时模拟方法[J]. 信号处理, 2018, 34(3): 368–375. doi: 10.16798/j.issn.1003-0530.2018.03.014. LI Hao, ZHU Qiuming, CHEN Yingbing, et al. A real-time FPGA-based emulation method for no-stationary channel fading[J]. Journal of Signal Processing, 2018, 34(3): 368–375. doi: 10.16798/j.issn.1003-0530.2018.03.014.
[35]	CHAUDHARI A, SQUIRES D, and TILGHMAN P. Colosseum: A battleground for AI let loose on the RF spectrum[J]. Microwave Journal, 2018, 61(9): 22–36.
[36]	HUANG Pengda, TONNEMACHER M J, DU Yongjiu, et al. Towards massive MIMO channel emulation: Channel accuracy versus implementation resources[J]. IEEE Transactions on Vehicular Technology, 2020, 69(5): 4635–4651. doi: 10.1109/TVT.2020.2980583.
[37]	HUANG Duoxian, XIN Lijian, HUANG Jie, et al. Adaptive non-stationary vehicle-to-vehicle MIMO channel simulator and emulator[C]. 2023 IEEE Wireless Communications and Networking Conference, Glasgow, UK, 2023: 1–6. doi: 10.1109/WCNC55385.2023.10119045.
[38]	VAN TIEN T, TIEN T M, and KHAI L D. Hardware implementation of a MIMO channel emulator for high speed WLAN 802.11 ac[C]. 2018 5th NAFOSTED Conference on Information and Computer Science, Ho Chi Minh City, Vietnam, 2018: 183–188. doi: 10.1109/NICS.2018.8606847.
[39]	FANG Sheng, MAO Tongbao, HUA Boyu, et al. A scalable spatial–temporal correlated non-stationary channel fading generation method[J]. Electronics, 2023, 12(19): 4132. doi: 10.3390/electronics12194132.
[40]	CHEN Yanning, LIU Fang, GAO Jie, et al. Research on electromagnetic environment characteristic acquisition system for industrial chips[J]. Electronics, 2024, 13(10): 1963. doi: 10.3390/electronics13101963.
[41]	XU Yuan, WU Jintie, LIANG Wei, et al. The development of high performance GNSS RF record & playback system[C]. 2017 International Workshop on Electromagnetics: Applications and Student Innovation Competition, London, UK, 2017: 74–78. doi: 10.1109/iWEM.2017.7968789.
[42]	CONSOLI A and YOSSEF Y B. High precision record & playback system for the analysis of wide-band GNSS signals[C]. 2019 European Navigation Conference, Warsaw, Poland, 2019: 1–5. doi: 10.1109/EURONAV.2019.8714139.
[43]	ZHAO Yingxiao, SU Yang, HUANG Rui, et al. Design and implementation of a radar waveform playback system for real-time digital signal processing test[C]. 2017 Sixth Asia-Pacific Conference on Antennas and Propagation, Xi'an, China, 2017: 1–3. doi: 10.1109/APCAP.2017.8420895.
[44]	MATHUR N and LAKSHMI B. High throughput arbitrary sample rate converter for software radios[C]. 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies, Kanyakumari, India, 2014: 1121–1123. doi: 10.1109/ICCICCT.2014.6993129.
[45]	ZHAO Wenhao, TIAN Shulin, LIU Ke, et al. Low spurious waveform synthesis based on digital resampling[C]. 2021 IEEE 15th International Conference on Electronic Measurement & Instruments, Nanjing, China, 2021: 276–280. doi: 10.1109/ICEMI52946.2021.9679594.
[46]	ZHAO Yingxiao, YOU Hongliang, and ZHANG Yue. An FPGA-based direct sampling and digital processing system for wideband and narrowband radar signal[J]. Journal of Physics: Conference Series, 2020, 1624(3): 032029. doi: 10.1088/1742-6596/1624/3/032029.
[47]	JENSERUD T and OTNES R. Reverberation tail in power delay profiles: Effects and modeling[C]. 2013 MTS/IEEE OCEANS-Bergen, Bergen, Norway, 2013: 1–10. doi: 10.1109/OCEANS-Bergen.2013.6608063.
[48]	SOCHELEAU F X, LAOT C, and PASSERIEUX J M. Parametric replay-based simulation of underwater acoustic communication channels[J]. IEEE Journal of Oceanic Engineering, 2015, 40(4): 796–806. doi: 10.1109/JOE.2015.2458211.
[49]	OTNES R, VAN WALREE P A, and JENSERUD T. Validation of replay-based underwater acoustic communication channel simulation[J]. IEEE Journal of Oceanic Engineering, 2013, 38(4): 689–700. doi: 10.1109/JOE.2013.2262743.
[50]	ISUKAPALLI Y, SONG H C, and HODGKISS W S. Stochastic channel simulator based on local scattering functions[J]. The Journal of the Acoustical Society of America, 2011, 130(4): EL200–EL205. doi: 10.1121/1.3633688.
[51]	SOCHELEAU F X, LAOT C, and PASSERIEUX J M. Stochastic replay of non-WSSUS underwater acoustic communication channels recorded at sea[J]. IEEE Transactions on Signal Processing, 2011, 59(10): 4838–4849. doi: 10.1109/TSP.2011.2160057.
[52]	YANG S, DEANE G B, PREISIG J C, et al. On the reusability of postexperimental field data for underwater acoustic communications R&D[J]. IEEE Journal of Oceanic Engineering, 2019, 44(4): 912–931. doi: 10.1109/JOE.2019.2925921.
[53]	YANG S and SINGER A C. Optimal replay-based channel simulation via dithering methods[C]. 2019 53rd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, USA, 2019: 957–963. doi: 10.1109/IEEECONF44664.2019.9049034.
[54]	OTNES R, VAN WALREE P A, BUEN H, et al. Underwater acoustic network simulation with lookup tables from physical-layer replay[J]. IEEE Journal of Oceanic Engineering, 2015, 40(4): 822–840. doi: 10.1109/JOE.2015.2471736.
[55]	RUSCA R, RAVIGLIONE F, CASETTI C, et al. Mobile RF scenario design for massive-scale wireless channel emulators[C]. 2023 Joint European Conference on Networks and Communications & 6G Summit, Gothenburg, Sweden, 2023: 675–680. doi: 10.1109/EuCNC/6GSummit58263.2023.10188319.
[56]	VILLA D, TEHRANI-MOAYYED M, ROBINSON C P, et al. Colosseum as a digital twin: Bridging real-world experimentation and wireless network emulation[J]. IEEE Transactions on Mobile Computing, 2024, 23(10): 9150–9166. doi: 10.1109/TMC.2024.3359596.
[57]	GHIAASI G, ASHURY M, VLASTARAS D, et al. Real-time vehicular channel emulator for future conformance tests of wireless ITS modems[C]. 2016 10th European Conference on Antennas and Propagation, Davos, Switzerland, 2016: 1–5. doi: 10.1109/EuCAP.2016.7481226.
[58]	CHAUDHARI A and BRAUN M. A scalable FPGA architecture for flexible, large-scale, real-time RF channel emulation[C]. 2018 13th International Symposium on Reconfigurable Communication-centric Systems-on-Chip, Lille, France, 2018: 1–8. doi: 10.1109/ReCoSoC.2018.8449390.
[59]	ZHOU Shun, OU Gang, and TANG Xiaomei. Satellite navigation multipath channel sparse reconstruction scheme applied in performance evaluation of constellation channel emulation[C]. 2021 13th International Symposium on Antennas, Propagation and EM Theory, Zhuhai, China, 2021: 01–03. doi: 10.1109/ISAPE54070.2021.9753259.
[60]	TEHRANI-MOAYYED M, BONATI L, JOHARI P, et al. Creating RF scenarios for large-scale, real-time wireless channel emulators[C]. 2021 19th Mediterranean Communication and Computer Networking Conference, Ibiza, Spain, 2021: 1–8. doi: 10.1109/MedComNet52149.2021.9501275.
[61]	MBUGUA A W, CHEN Yun, and FAN Wei. On simplification of ray tracing channels in radio channel emulators for device testing[C]. 2021 15th European Conference on Antennas and Propagation, Dusseldorf, Germany, 2021: 1–5. doi: 10.23919/EuCAP51087.2021.9411504.
[62]	MBUGUA A W, CHEN Yun, and FAN Wei. Radio channel emulation for virtual drive testing with site-specific channels[C]. 2022 16th European Conference on Antennas and Propagation, Madrid, Spain, 2022: 1–5. doi: 10.23919/EuCAP53622.2022.9769382.
[63]	MBUGUA A W, CHEN Yun, RASCHKOWSKI L, et al. Efficient preprocessing of site-specific radio channels for virtual drive testing in hardware emulators[J]. IEEE Transactions on Aerospace and Electronic Systems, 2023, 59(2): 1787–1799. doi: 10.1109/TAES.2022.3205289.
[64]	GHIAASI G, BLAZEK T, ASHURY M, et al. Real‐time emulation of nonstationary channels in safety‐relevant vehicular scenarios[J]. Wireless Communications and Mobile Computing, 2018, 2018: 2423837. doi: 10.1155/2018/2423837.
[65]	HOFER M, BERNADÓ L, RAINER B, et al. Evaluation of vehicle-in-the-loop tests for wireless V2X communication[C]. 2019 IEEE 90th Vehicular Technology Conference, Honolulu, USA, 2019: 1–5. doi: 10.1109/VTCFall.2019.8891080.
[66]	BARCKLOW D R, BLOCH L E, SWEENEY S W, et al. Radio frequency emulation system for the defense advanced research projects agency spectrum collaboration challenge[J]. Johns Hopkins APL Technical Digest, 2019, 35(1): 69–78.
[67]	KALTENBERGER F, ZEMEN T, and UEBERHUBER C W. Low-complexity geometry-based MIMO channel simulation[J]. EURASIP Journal on Advances in Signal Processing, 2007, 2007: 095281. doi: 10.1155/2007/95281.
[68]	HOFER M, XU Zhinan, VLASTARAS D, et al. Real-time geometry-based wireless channel emulation[J]. IEEE Transactions on Vehicular Technology, 2019, 68(2): 1631–1645. doi: 10.1109/TVT.2018.2888914.
[69]	DAKIĆ A, HOFER M, RAINER B, et al. Real-time vehicular wireless system-level simulation[J]. IEEE Access, 2021, 9: 23202–23217. doi: 10.1109/ACCESS.2021.3055978.
[70]	ZHANG Dongyang, MAO Kai, YANG Yang, et al. Implementation of non-stationary channel emulator based on USRP[C]. 5th International Conference on Machine Learning and Intelligent Communications, Shenzhen, China, 2021, 342: 437–446. doi: 10.1007/978-3-030-66785-6_48.
[71]	黄文清, 李伟东, 郭放, 等. 基于轨迹的车对车无线信道建模及硬件模拟[J]. 电子测量与仪器学报, 2019, 33(8): 55–62. doi: 10.13382/j.jemi.B1902193. HUANG Wenqing, LI Weidong, GUO Fang, et al. Channel modeling and hardware emulation for the trajectories based vehicle-to-vehicle channels[J]. Journal of Electronic Measurement and Instrumentation, 2019, 33(8): 55–62. doi: 10.13382/j.jemi.B1902193.
[72]	CHEN Y M and CHEN Chuncheng. Design of farrow structured variable fractional delay filter for time-varying LEO communication channel emulator with SRRC communication waveforms[J]. IEEE Access, 2024, 12: 122229–122238. doi: 10.1109/ACCESS.2024.3452496.
[73]	YOUNG D J and BEAULIEU N C. The generation of correlated Rayleigh random variates by inverse discrete Fourier transform[J]. IEEE Transactions on Communications, 2000, 48(7): 1114–1127. doi: 10.1109/26.855519.
[74]	BADDOUR K E and BEAULIEU N C. Autoregressive modeling for fading channel simulation[J]. IEEE Transactions on Wireless Communications, 2005, 4(4): 1650–1662. doi: 10.1109/TWC.2005.850327.
[75]	ALIMOHAMMAD A and COCKBURN B F. A reconfigurable SOS-based Rayleigh fading channel simulator[C]. IEEE Workshop on Signal Processing Systems Design and Implementation, Banff, Canada, 2006: 39–44. doi: 10.1109/SIPS.2006.352552.
[76]	YUAN Yi, WANG Chengxiang, CHENG Xiang, et al. Novel 3D geometry-based stochastic models for non-isotropic MIMO vehicle-to-vehicle channels[J]. IEEE Transactions on Wireless Communications, 2014, 13(1): 298–309. doi: 10.1109/TWC.2013.120313.130434.
[77]	GUTIÉRREZ C A and PATZOLD M. The design of sum-of-cisoids Rayleigh fading channel simulators assuming non-isotropic scattering conditions[J]. IEEE Transactions on Wireless Communications, 2010, 9(4): 1308–1314. doi: 10.1109/TWC.2010.04.091198.
[78]	WANG Weimin, WANG Heng, WU Yongle, et al. Novel deterministic angular sampling methods for 3D channel models[J]. IEEE Communications Letters, 2021, 25(6): 1756–1760. doi: 10.1109/LCOMM.2021.3061735.
[79]	ZHU Qiuming, HUANG Wei, MAO Kai, et al. A flexible FPGA-based channel emulator for non-stationary MIMO fading channels[J]. Applied Sciences, 2020, 10(12): 4161. doi: 10.3390/app10124161.
[80]	LIU Xinglin, ZHU Qiuming, CHEN Xiaomin, et al. A new simulation model for non-stationary fading channel[C]. 2016 3rd International Conference on Electronic Design, Phuket, Thailand, 2016: 66–69. doi: 10.1109/ICED.2016.7804608.
[81]	ZHU Qiuming, LI Hao, FU Yu, et al. A novel 3D non-stationary wireless MIMO channel simulator and hardware emulator[J]. IEEE Transactions on Communications, 2018, 66(9): 3865–3878. doi: 10.1109/TCOMM.2018.2824817.
[82]	GUTIÉRREZ C A and PÄTZOLD M. The generalized method of equal areas for the design of sum-of-cisoids simulators for mobile Rayleigh fading channels with arbitrary Doppler spectra[J]. Wireless Communications and Mobile Computing, 2013, 13(10): 951–966. doi: 10.1002/wcm.1154.
[83]	ZHANG Yuxiang, YUAN Zhiqiang, TIAN Lei, et al. A novel random angular sampling method for spatial and temporal channel emulation[J]. IEEE Wireless Communications Letters, 2019, 8(5): 1381–1385. doi: 10.1109/LWC.2019.2918787.
[84]	GUTIÉRREZ C A, FABÍAN-RODRÍGUEZ R A, CASTILLO-SORIA F R, et al. SOC-based simulation of 3D MIMO mobile-to-mobile fading channels: A Riemann sum approach[J]. IEEE Open Journal of Vehicular Technology, 2024, 5: 1–20. doi: 10.1109/OJVT.2023.3331534.
[85]	PÄTZOLD M and YOUSSEF N. Modelling and simulation of direction-selective and frequency-selective mobile radio channels[J]. AEU - International Journal of Electronics and Communications, 2001, 55(6): 433–442. doi: 10.1078/1434-8411-54100064.
[86]	DONG Shuli, ZHANG Taotao, and WANG Yan. A real-time simulation design of multi-path fading channel based on SOS method[C]. 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference, Chengdu, China, 2019: 2550–2554. doi: 10.1109/IAEAC47372.2019.8997884.
[87]	HUANG Duoxian, XIN Lijian, HUANG Jie, et al. A non-stationary channel emulator for 6G THz wireless channels[C]. 2023 International Conference on Wireless Communications and Signal Processing, Hangzhou, China, 2023: 563–568. doi: 10.1109/WCSP58612.2023.10405337.
[88]	ZHAO Zikun, ZHU Qiuming, MAO Kai, et al. An efficient hardware generator for massive non-stationary fading channels[C]. 2020 IEEE Globecom Workshops, Taipei, China, 2020: 1–6. doi: 10.1109/GCWkshps50303.2020.9367588.
[89]	FANG Chen, MAO Kai, FANG Sheng, et al. CORDIC-based general multiple fading generator for wireless channel digital twin[J]. Sensors, 2023, 23(5): 2712. doi: 10.3390/s23052712.
[90]	赵子坤, 房晨, 陈小敏, 等. 面向5G/6G大规模MIMO信道实时模拟研究[J]. 微波学报, 2022, 38(1): 30–35,40. doi: 10.14183/j.cnki.1005-6122.202201007. ZHAO Zikun, FANG Chen, CHEN Xiaomin, et al. A real-time emulation research on 5G/6G massive MIMO channels[J]. Journal of Microwaves, 2022, 38(1): 30–35,40. doi: 10.14183/j.cnki.1005-6122.202201007.
[91]	YANG Yang, LI Tingpeng, CHEN Xiaomin, et al. Real-time ray-based channel generation and emulation for UAV communications[J]. Chinese Journal of Aeronautics, 2022, 35(9): 106–116. doi: 10.1016/j.cja.2021.12.008.
[92]	PAPALAMPROU I, ARMENIAKOS G, STRATAKOS I, et al. Flexible real-time emulation of fading channels on SoC-FPGA devices[C]. 2024 Panhellenic Conference on Electronics & Telecommunications, Thessaloniki, Greece, 2024: 1–6. doi: 10.1109/PACET60398.2024.10497075.
[93]	XIAO Han, TIAN Wenqiang, LIU Wendong, et al. ChannelGAN: Deep learning-based channel modeling and generating[J]. IEEE Wireless Communications Letters, 2022, 11(3): 650–654. doi: 10.1109/LWC.2021.3140102.
[94]	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.
[95]	MAO Kai, ZHU Qiuming, SONG Maozhong, et al. Machine-learning-based 3-D channel modeling for U2V mmWave communications[J]. IEEE Internet of Things Journal, 2022, 9(18): 17592–17607. doi: 10.1109/JIOT.2022.3155773.