无线信道硬件孪生技术研究进展与挑战

房胜; 朱秋明; 谢悦天; 江浩; 李辉; 吴启晖; 毛开; 华博宇

doi:10.11999/JEIT241093

无线信道硬件孪生技术研究进展与挑战

doi: 10.11999/JEIT241093 cstr: 32379.14.JEIT241093

房胜¹,
朱秋明^1, ,,
谢悦天¹,
江浩²,
李辉¹,
吴启晖¹,
毛开¹,
华博宇¹

1.
南京航空航天大学电磁频谱空间认知动态系统重点实验室南京 211106
2.
东南大学移动通信国家重点实验室南京 210096

基金项目: 国家自然科学基金(62271250, U23B2005)

详细信息

作者简介:
房胜：男，博士生，研究方向为无线信道建模与孪生技术

朱秋明：男，教授，研究方向为无线信道测量建模与数字孪生、电磁频谱态势可视化测绘与认知等

谢悦天：男，硕士生，研究方向为无线信道模拟技术

江浩：男，副教授，研究方向为低空无人机通感算一体化理论与关键技术

李辉：男，硕士生，研究方向为无线信道建模

吴启晖：男，教授，研究方向为认知信息论、电磁空间频谱智能管控、天地一体化信息网络等

毛开：男，博士，研究方向为信道测量与建模

华博宇：男，讲师，研究方向为移动无线信道测量与参数估计

通讯作者:
朱秋明　zhuqiuming@nuaa.edu.cn

中图分类号: TN92
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- 被引次数: 0
出版历程
- 收稿日期: 2024-12-10
- 修回日期: 2025-07-17
- 网络出版日期: 2025-07-24
- 刊出日期: 2025-08-27

Advances and Challenges in Wireless Channel Hardware Twin

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)

摘要

摘要: 无线信道特性对通信系统性能至关重要，信道孪生技术是指通过物理或数字方式精确地复现信道特性对信号传播的失真影响，为通信系统性能评估提供有效测试手段。其中，数字孪生方式更为灵活方便，特别是无线信道硬件孪生平台已广泛应用于通信设备的批量化测试。然而，随着通信系统向大带宽、高动态和大规模网络发展，现有无线信道硬件孪生技术难以满足未来系统性能验证需求。鉴于此，该文首先给出了硬件孪生技术面临的主要挑战，然后对国内外研究进展进行分类阐述与分析比较，最后探讨了无线信道硬件孪生技术的未来发展趋势和应用前景。
- 无线信道 /
- 数字孪生 /
- 信道硬件孪生 /
- 信道特性 /
- 通信性能测试
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

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图 1 无线信道硬件孪生技术研究内容

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图 2 基于实测信道数据的硬件孪生架构

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图 3 面向确定性信道模型的硬件孪生架构

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图 4 面向统计性信道模型的硬件孪生架构

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表 1 部分典型的无线信道数字孪生系统

开发团队	系统名称	类型	系统特点
美国纽约大学^[7]	NYUSIM	CS	基于MATLAB平台；支持0.5～150 GHz频段信道建模；支持UMi, UMa, RMa, InH和InF场景；支持基于真实测量的确定信道模型
德国弗朗霍夫海因里希赫兹研究所^[8]	QuaDRiGa	CS	基于MATLAB平台；支持0.5～100 GHz频段信道建模；支持UMi, UMa, RMa, 、InH和卫星场景；支持3GPP和WINNER+等信道模型
阿卜杜拉国王科技大学^[9]	TeraMIMO	CS	基于MATLAB平台；支持0.1～10 THz频段信道建模；支持室内/室外短程通信场景；支持宽带太赫兹信道模型
乌拉圭共和国大学^[10]	PyWiCh	CS	基于MATLAB平台；支持0.5～100 GHz 频段信道建模；支持UMi, UMa, InH和InF场景；支持3GPP信道模型
任康无线智能实验室^[11]	DeepMIMO	CS	基于MATLAB和Python平台；支持毫米波频段信道的建模；支持DeepMIMO网站提供的确定场景；支持基于射线追踪的确定性信道模型
北京邮电大学^[12]	BUPTCMG-IMT2030	CS	基于MATLAB平台；支持0.5～132 GHz频段信道建模；支持UMi与InH等典型场景；支持ITU-R信道模型
北京交通大学^[13]	CloudRT	CS	基于MATLAB和C++等平台；支持sub-6 GHz、毫米波即THz等频段信道建模；支持铁路、城市室内和室外等场景；支持基于射线追踪的确定性信道模型
东南大学^[14]	SEU-PML-6GPCS	CS	基于MATLAB平台；支持6G全频段信道建模；支持卫星、无人机、陆地和海洋等场景；支持3GPP和ITU-R等信道模型
美国国防高级研究计划局&美国东北大学^[15]	Colosseum	CE	基于NI USRP平台；支持10 MHz-6 GHz射频信号接入；支持100 MHz带宽信号处理；时延精度可达1 ms；最大支持256×256通信网络验证
奥地利理工学院^[16]	AIT CE	CE	基于NI USRP平台；支持sub-6 GHz射频信号接入；支持20 MHz带宽信号处理；时延精度可达100 ns
南京航空航天大学^[17]	NUAA-UAVChEm	CE	基于FPGA平台；支持sub-6 GHz的射频信号接入；支持100 MHz带宽信号处理；最大支持8×8通信网络验证
是德科技^[18]	PROPSIM F64	CE	基于FPGA平台；支持3 MHz～43.5 GHz射频信号接入；支持400 MHz带宽信号处理；最大支持16×16通信网络验证
坤恒顺维^[19]	KSW-WNS04A	CE	基于FPGA平台；支持1.5 MHz～7.5 GHz射频信号接入；支持1 GHz带宽信号处理；时延精度可达0.1 ns
思博伦^[20]	Vertex	CE	基于FPGA平台；支持30 MHz～5.9 GHz射频信号接入；支持1.2 GHz带宽信号处理；最大支持64×8通信网络验证
罗德&施瓦茨^[21]	SMW200A	CE	基于FPGA平台；支持100 kHz～3 GHz射频信号接入；支持160 MHz带宽信号处理；最大支持8×8通信网络验证
注：第三代合作伙伴计划(3rd Generation Partnership Project, 3GPP)；国际电信联盟(International Telecommunication Union, ITU)；无线世界研究论坛(Wireless World Initiative New Radio, WINNER)；城市微小区(Urban Microcell, UMi)；城市宏小区(Urban Macrocell, UMa)；农村宏小区(Rural Macrocell, RMa)；室内热点(Indoor Hotspot, InH)；室内工厂(Indoor Factory, InF)；通用软件无线电外设(Universal Software Radio Peripheral, USRP)；现场可编程门阵列(Field Programmable Gate Array, FPGA)

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表 2 3种信道硬件孪生技术性能比对

	真实性	灵活性	可扩展性	易实现性	仿真时长
面向实测信道数据的信道孪生	$\bigstar\bigstar\bigstar $	$\bigstar $	$\bigstar $	$\bigstar \bigstar\bigstar$	$\bigstar $
面向确定性信道模型的信道孪生	$\bigstar\bigstar $	$\bigstar\bigstar\bigstar $	$\bigstar\bigstar $	$\bigstar\bigstar $	$\bigstar\bigstar $
面向统计性信道模型的信道孪生	$\bigstar $	$\bigstar\bigstar\bigstar $	$\bigstar\bigstar\bigstar $	$\bigstar $	$\bigstar\bigstar\bigstar $
注：$\bigstar $越多表示该项性能越好；真实性：对真实传播场景的复现能力；灵活性：是否支持多场景仿真；可扩展性：是否支持根据需求进行硬件升级或扩展；易实现性：实现难度和资源需求；仿真时长：相同资源下，可支持的信道仿真的时长

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