Concept of Embodied Intelligence in the Electromagnetic Spectrum, Current Research Status, and Development Trends

KUANG Yixin; LI Xianbin; QIN Junxiang; MA Chao; QU Zhi; CHEN Jianyun; LIU Kaibo

doi:10.12263/DZXB.20251178

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Concept of Embodied Intelligence in the Electromagnetic Spectrum, Current Research Status, and Development Trends

更新时间：2026-06-16

- Concept of Embodied Intelligence in the Electromagnetic Spectrum, Current Research Status, and Development Trends
- ACTA ELECTRONICA SINICA Pages: 1-36(2026)
- 作者机构：
  
  中国人民解放军国防科技大学智能科学学院，湖南长沙 410073
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62403481)
- DOI：10.12263/DZXB.20251178
  CLC： TN97;TP18
- Received：02 February 2026，
  
  Accepted：24 February 2026，
  
  Online First：16 June 2026，
- 稿件说明：
移动端阅览
KUANG Yixin, LI Xianbin, QIN Junxiang, et al. Concept of Embodied Intelligence in the Electromagnetic Spectrum, Current Research Status, and Development Trends[J/OL]. ACTA ELECTRONICA SINICA, 2026, 1-36.
DOI：

KUANG Yixin, LI Xianbin, QIN Junxiang, et al. Concept of Embodied Intelligence in the Electromagnetic Spectrum, Current Research Status, and Development Trends[J/OL]. ACTA ELECTRONICA SINICA, 2026, 1-36. DOI： 10.12263/DZXB.20251178.

摘要

电磁频谱作为无线通信与感知的核心资源，在智能化技术的推动下正经历着从被动管理向主动认知的范式跃迁。电磁频谱具身智能通过将智能体与电磁环境深度耦合，实现对频谱资源的自主感知、认知与控制。本文系统综述了电磁频谱具身智能的理论框架、关键技术与应用前景。从具身认知理论出发，论证了“电磁器官”作为智能体身体组成部分的合理性，将电磁频谱重新定义为与三维物理空间并列的关键环境维度，构建了电磁具身闭环的数学模型，揭示了电磁频谱具身智能“智能源于身体与环境动态耦合”的本质。基于“感知—认知—行动”三层架构，系统分析了电磁频谱具身智能的核心使能技术：在电磁感知层，从信号域、空间域、多域融合三个维度阐述了软件定义无线电、认知雷达、协同频谱感知、多模态融合感知等关键技术如何实现从物理信号到语义理解的跨越；在认知与决策层，针对载体约束与多载体协同两类场景，详细分析了轻量级强化学习、在线元学习、多智能体协同决策、博弈论优化等方法如何支撑智能体在动态对抗环境中的自主决策与群体协同；在行动与反馈层，从电磁环境交互与具身载体重构两个方面，探讨了动态频谱接入、干扰对抗、波形重构、硬件可重构等技术如何将认知策略转化为电磁行为并形成闭环反馈。总结了电磁频谱具身智能在6G移动通信、低轨巨星座智能电磁体系、智慧城市频谱治理、智能战场电磁作战等典型场景中的应用实践与技术优势。针对当前研究面临的实时性约束、样本效率、安全鲁棒性、可解释性等挑战，展望了电磁频谱具身智能的未来发展趋势，包括标准化体系建设、产业生态培育、跨学科理论融合、大规模工程部署等方向。本文的研究为电磁频谱智能化应用提供了系统性的理论参考与技术路线，对推动电磁频谱治理体系现代化和支撑国家信息基础设施建设具有重要意义。

Abstract

As the core resource for wireless communications and sensing

the electromagnetic spectrum is undergoing a paradigm shift from passive management to active cognition under the impetus of intelligent technologies. Electromagnetic-spectrum embodied intelligence deeply couples the agent with the electromagnetic environment to achieve autonomous sensing

cognition

and control of spectrum resources. This paper provides a systematic review of the theoretical framework

enabling technologies

and application prospects of electromagnetic-spectrum embodied intelligence. Starting from embodied-cognition theory

it argues for the legitimacy of “electromagnetic organs” as components of the body of an agent

reconceptualizes the electromagnetic spectrum as a key environmental dimension on par with three-dimensional physical space

constructs a mathematical model of the electromagnetic embodied closed loop

and reveals the essence that intelligence emerges from the dynamic coupling between body and environment. Building on a three-layer “sensing-cognition-action” architecture

we analyze core enabling technologies: at the electromagnetic sensing layer

across the signal

spatial

and multi-domain-fusion dimensions

we explain how software-defined radio

cognitive radar

cooperative spectrum sensing

and multimodal fusion enable a leap from physical signals to semantic understanding; at the cognition and decision-making layer

for both platform-constrained and multi-platform collaborative scenarios

we examine how lightweight reinforcement learning

online meta-learning

multi-agent collaborative decision-making

and game-theoretic optimization support autonomous decision-making and collective coordination in dynamic

adversarial environments; at the action-and-feedback layer

from electromagnetic-environment interaction to embodied-platform reconfiguration

we discuss how dynamic spectrum access

jamming and counter-jamming

waveform reconfiguration

and hardware reconfigurable technology translate cognitive policies into electromagnetic behaviors and close the loop. We summarize application practices and advantages in representative scenarios such as 6G mobile communications

intelligent electromagnetic system of LEO mega-constellations spectrum governance for smart cities

and electromagnetic operations on intelligent battlefields. In view of current challenges—including real-time constraints

sample efficiency

safety and robustness

and interpretability—we outline future trends involving standardization

ecosystem development

cross-disciplinary theoretical integration

and large-scale engineering deployment. The study offers a systematic theoretical reference and a technical roadmap for intelligent spectrum management

contributing to the modernization of spectrum-governance systems and supporting the construction of national information infrastructure.

关键词

Keywords

references

孙健 , 程龙 , 贺威 , 等 . 具身智能专题简介 [J ] . 中国科学: 信息科学 , 2025 , 55 ( 5 ): 1251 - 1252 . DOI: 10.1360/ssi-2025-0177 http://dx.doi.org/10.1360/ssi-2025-0177

Sun Jian , Cheng Long , He Wei , et al . Special topic: Embodied intelligence [J ] . Scientia Sinica Informationis , 2025 , 55 ( 5 ): 1251 - 1252 . (in Chinese) . DOI: 10.1360/ssi-2025-0177 http://dx.doi.org/10.1360/ssi-2025-0177

Turing A M . I.—computing machinery and intelligence [J ] . Mind , 1950 , LIX ( 236 ): 433 - 460 . DOI: 10.1093/mind/lix.236.433 http://dx.doi.org/10.1093/mind/lix.236.433

Brooks R A . Intelligence without representation [J ] . Artificial Intelligence , 1991 , 47 ( 1/2/3 ): 139 - 159 . DOI: 10.1016/0004-3702(91)90053-M http://dx.doi.org/10.1016/0004-3702(91)90053-M .

Pfeifer R , Iida F . Embodied artificial intelligence: Trends and challenges [M ] //Iida F, Pfeifer R, Steels L, et al. Embodied artificial intelligence . Berlin : Springer , 2004 : 1 - 26 . DOI: 10.1007/978-3-540-27833-7_1 http://dx.doi.org/10.1007/978-3-540-27833-7_1 .

Roy N , Posner I , Barfoot T , et al . From machine learning to robotics: Challenges and opportunities for embodied intelligence [EB/OL ] . ( 2021-10-28 )[ 2026-05-21 ] . https://doi.org/10.48550/arXiv.2110.15245 https://doi.org/10.48550/arXiv.2110.15245 .

王文晟 , 谭宁 , 黄凯 , 等 . 基于大模型的具身智能系统综述 [J ] . 自动化学报 , 2025 , 51 ( 1 ): 1 - 19 . DOI: 10.16383/j.aas.c240542 http://dx.doi.org/10.16383/j.aas.c240542 .

Wang Wensheng , Tan Ning , Huang Kai , et al . Embodied intelligence systems based on large models: A survey [J ] . Acta Automatica Sinica , 2025 , 51 ( 1 ): 1 - 19 . (in Chinese)

温敬朋 , 杨健 , 王沙飞 . 电子战装备技术发展现状与展望 [J ] . 信息对抗技术 , 2022 , 1 ( 1 ): 1 - 10 .

Wen Jingpeng , Yang Jian , Wang Shafei . Development status and prospect of electronic warfare equipment technology [J ] . Information Countermeasure Technology , 2022 , 1 ( 1 ): 1 - 10 . (in Chinese)

Li Pengfei , Fan Jiaxin , Wu Jianhong . Exploring the key technologies and applications of 6G wireless communication network [J ] . iScience , 2025 , 28 ( 5 ): 112281 . DOI: 10.1016/j.isci.2025.112281 http://dx.doi.org/10.1016/j.isci.2025.112281 .

苗可可 . 针对弱信号检测的抗干扰方法研究 [D ] . 长沙 : 国防科技大学 , 2014 .

Miao Keke . Research on interference mitigation methods based on weak signal detection [D ] . Changsha : National University of Defense Technology , 2014 . (in Chinese)

金亚秋 , 徐丰 . 面向未来空间电磁信息技术的综合分析 [J ] . 中国科学基金 , 2021 , 35 ( 5 ): 688 - 693 .

Jin Yaqiu , Xu Feng . General analysis for future spatial electromagnetic information technologies [J ] . Bulletin of National Natural Science Foundation of China , 2021 , 35 ( 5 ): 688 - 693 . (in Chinese)

丁国如 , 孙佳琛 , 王海超 , 等 . 复杂电磁环境下频谱智能管控技术探讨 [J ] . 航空学报 , 2021 , 42 ( 4 ): 524750 .

Ding Guoru , Sun Jiachen , Wang Haichao , et al . Discussion on technologies for intelligent spectrum management and control under complex electromagnetic environments [J ] . Acta Aeronautica et Astronautica Sinica , 2021 , 42 ( 4 ): 524750 . (in Chinese)

He Cuiwei , Ali W . Advances in visible light communication [J ] . Photonics , 2023 , 10 ( 11 ): 1277 . DOI: 10.3390/photonics10111277 http://dx.doi.org/10.3390/photonics10111277

Rodwell M . Ultra high frequency electronics and near-THz semiconductor devices: Emerging technologies, applications, propagation properties [R/OL ] . Santa Barbara : University of California , Santa Barbara, 2004 [ 2026-05-21 ] . https://web.ece.ucsb.edu/Faculty/rodwell/publications_and_presentations/seminars/rodwell_submmwave_technology.pdf https://web.ece.ucsb.edu/Faculty/rodwell/publications_and_presentations/seminars/rodwell_submmwave_technology.pdf .

Vold K . The parity argument for extended consciousness [J ] . Journal of Consciousness Studies , 2015 , 22 ( 3/4 ): 16 - 33 .

Shen Feng , Wang Zheng , Ding Guoru , et al . 3D compressed spectrum mapping with sampling locations optimization in spectrum-heterogeneous environment [J ] . IEEE Transactions on Wireless Communications , 2022 , 21 ( 1 ): 326 - 338 . DOI: 10.1109/TWC.2021.3095342 http://dx.doi.org/10.1109/TWC.2021.3095342 .

Laine M . Design of a software-defined radio (SDR) system for real-time signal processing in cognitive radio networks using GNU radio [J ] . International Journal of Electrical and Data Communication , 2025 , 6 ( 1 ): 1 - 6 . DOI: 10.22271/27083969.2025.v6.i1a.66 http://dx.doi.org/10.22271/27083969.2025.v6.i1a.66 .

Zhang Xiaowen , Liu Xingzhao . Adaptive waveform design for cognitive radar in multiple targets situation [J ] . Entropy , 2018 , 20 ( 2 ): 114 . DOI: 10.3390/e20020114 http://dx.doi.org/10.3390/e20020114 .

Chen Jingye , Li Ziyu , Chen Lei , et al . High-speed real-time spectrum analysis system based on FPGA and GPU parallel arithmetic [C ] // Proceedings of the 4th International Conference on Machinery, Materials and Computing Technology . Paris : Atlantis Press , 2016 : 1091 - 1094 . https://doi.org/10.2991/icmmct-16.2016.215 https://doi.org/10.2991/icmmct-16.2016.215 .

Ambrosini E , Scorolli C , Borghi A M , et al . Which body for embodied cognition affordance and language within actual and perceived reaching space [J ] . Consciousness and Cognition , 2012 , 21 ( 3 ): 1551 - 1557 . DOI: 10.1016/j.concog.2012.06.010 http://dx.doi.org/10.1016/j.concog.2012.06.010 .

Hardcastle V G . The consciousness of embodied cognition, affordances, and the brain [J ] . Topoi , 2020 , 39 ( 1 ): 23 - 33 . DOI: 10.1007/s11245-017-9503-7 http://dx.doi.org/10.1007/s11245-017-9503-7 .

靳立民 , 王海超 , 顾江春 , 等 . 低空具身智能频谱管控技术研究 [J ] . 数据采集与处理 , 2025 , 40 ( 1 ): 45 - 55 .

Jin Limin , Wang Haichao , Gu Jiangchun , et al . Research on low-altitude embodied artificial intelligence-enabled spectrum management and control technology [J ] . Journal of Data Acquisition and Processing , 2025 , 40 ( 1 ): 45 - 55 . (in Chinese)

Wilson A D , Golonka S . Embodied cognition is not what you think it is [J ] . Frontiers in Psychology , 2013 , 4 : 58 . DOI: 10.3389/fpsyg.2013.00058 http://dx.doi.org/10.3389/fpsyg.2013.00058 .

Zhao Zikai , Wu Qiuxuan , Wang Jian , et al . Exploring embodied intelligence in soft robotics: A review [J ] . Biomimetics , 2024 , 9 ( 4 ): 248 . DOI: 10.3390/biomimetics9040248 http://dx.doi.org/10.3390/biomimetics9040248

Shah S I H , Shah S S , Bernhardsson E , et al . Shape memory alloy-based fluidically reconfigurable metasurfaced beam steering antenna [J ] . IEEE Access , 2023 , 11 : 102271 - 102278 . DOI: 10.1109/ACCESS.2023.3315318 http://dx.doi.org/10.1109/ACCESS.2023.3315318 .

Paracha K N , Butt A D , Alghamdi A S , et al . Liquid metal antennas: Materials, fabrication and applications [J ] . Sensors , 2020 , 20 ( 1 ): 177 . DOI: 10.3390/s20010177 http://dx.doi.org/10.3390/s20010177 .

Fu Yuan , Li Yuanbo , Fu Xiaojian , et al . A dual-broadband liquid-crystal programmable metasurface and its application in terahertz wireless communications [J ] . Engineering , 2026 , 59 : 352 - 363 . DOI: 10.1016/j.eng.2025.08.040 http://dx.doi.org/10.1016/j.eng.2025.08.040 .

Heiligenberg W . The jamming avoidance response in the weakly electric fish Eigenmannia [J ] . Naturwissenschaften , 1980 , 67 ( 10 ): 499 - 507 . DOI: 10.1007/BF01047630 http://dx.doi.org/10.1007/BF01047630 .

Gibson J J . The ecological approach to visual perception: Classic edition [M ] . New York : Psychology Press , 2014 . DOI: 10.4324/9781315740218 http://dx.doi.org/10.4324/9781315740218

Li G , Wang W , Wu Q H . Cognitive intelligent spectrum management and control for low earth orbit satellite system [J ] . ZTE Technology Journal , 2021 , 27 ( 5 ): 7 - 11 . DOI: 10.12142/ztetj.202105003 http://dx.doi.org/10.12142/ztetj.202105003 .

沈锋 , 丁国如 , 李婕 , 等 . 电磁频谱多维态势压缩测绘技术研究进展 [J ] . 通信学报 , 2023 , 44 ( 11 ): 25 - 42 . DOI: 10.11959/j.issn.1000-436x.2023174 http://dx.doi.org/10.11959/j.issn.1000-436x.2023174 .

Shen Feng , Ding Guoru , Li Jie , et al . Research progress on electromagnetic spectrum multidimensional situation compressed mapping technology [J ] . Journal on Communications , 2023 , 44 ( 11 ): 25 - 42 . DOI: 10.11959/j.issn.1000-436x.2023174. http://dx.doi.org/10.11959/j.issn.1000-436x.2023174. (in Chinese)

王欣 , 申滨 , 黄晓舸 . 基于重叠Ket增强和张量列车的非平衡频谱制图算法 [J ] . 电子学报 , 2024 , 52 ( 7 ): 2468 - 2476 .

Wang Xin , Shen Bin , Huang Xiaoge . Unbalanced spectrum cartography algorithm based on overlapping ket augmentation and tensor train [J ] . Acta Electronica Sinica , 2024 , 52 ( 7 ): 2468 - 2476 . (in Chinese)

Fink P W , Foo P S , Warren W H . Catching fly balls in virtual reality: A critical test of the outfielder problem [J ] . Journal of Vision , 2009 , 9 ( 13 ): 14 . DOI: 10.1167/9.13.14 http://dx.doi.org/10.1167/9.13.14 .

Sengupta K , Nagatsuma T , Mittleman D M . Terahertz integrated electronic and hybrid electronic-photonic systems [J ] . Nature Electronics , 2018 , 1 ( 12 ): 622 - 635 . DOI: 10.1038/s41928-018-0173-2 http://dx.doi.org/10.1038/s41928-018-0173-2

肖义德 . 认知无线电频谱感知技术研究 [D ] . 杭州 : 杭州电子科技大学 , 2021 . DOI: 10.27075/d.cnki.ghzdc.2021.000440 http://dx.doi.org/10.27075/d.cnki.ghzdc.2021.000440 .

Xiao Yide . Research on cognitive wireless spectrum sensing technology [D ] . Hangzhou : Hangzhou Dianzi University , 2021 . DOI: 10.27075/d.cnki.ghzdc.2021.000440. http://dx.doi.org/10.27075/d.cnki.ghzdc.2021.000440. (in Chinese)

Xiao Weixuan , Kaneko M , Rachkidy N EL , et al . Integrating LoRa collision decoding and MAC protocols for enabling IoT massive connectivity [J ] . IEEE Internet of Things Magazine , 2022 , 5 ( 3 ): 166 - 173 . DOI: 10.1109/IOTM.001.2200055 http://dx.doi.org/10.1109/IOTM.001.2200055 .

Hafez D T M I . Development of spectrum sharing protocol for cognitive radio internet of things [D ] . Avignon : University of Avignon; Cairo: American University in Cairo , 2020 .

Sun Wei , Yu Jiadi , Liu Tong . A distributed spectrum sharing algorithm in cognitive radio networks [C ] // 2014 20th IEEE International Conference on Parallel and Distributed Systems . Piscataway : IEEE , 2014 : 510 - 517 . DOI: 10.1109/PADSW.2014.7097848 http://dx.doi.org/10.1109/PADSW.2014.7097848 .

Gupta A , Kausar R , Tanwar S , et al . Efficient spectrum sharing in 5G and beyond network: A survey [J ] . Telecommunication Systems , 2025 , 88 ( 1 ): 29 . DOI: 10.1007/s11235-025-01261-7 http://dx.doi.org/10.1007/s11235-025-01261-7 .

AL-Habashna A , Menard J , Wainer G , et al . Decentralized and joint resource allocation, beamforming, and beamcombining for 5G networks with heterogeneous MARL [J ] . IEEE Access , 2025 , 13 : 101491 - 101506 . DOI: 10.1109/ACCESS.2025.3576190 http://dx.doi.org/10.1109/ACCESS.2025.3576190 .

Shen Bin , Zhao Chengshi , Zhou Zheng . User clusters based hierarchical cooperative spectrum sensing in cognitive radio networks [C ] // 2009 4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications . Piscataway : IEEE , 2009 : 1 - 6 . DOI: 10.1109/CROWNCOM.2009.5189384 http://dx.doi.org/10.1109/CROWNCOM.2009.5189384 .

Rajesh R , Darak S J , Jain A , et al . Hardware-software co-design of statistical and deep-learning frameworks for wideband sensing on Zynq system on chip [J ] . IEEE Transactions on Very Large Scale Integration (VLSI) Systems , 2023 , 31 ( 1 ): 79 - 89 . DOI: 10.1109/tvlsi.2022.3224582 http://dx.doi.org/10.1109/tvlsi.2022.3224582

Cho K W . Hardware-software co-design for programmable smart radio environments [C ] // Proceedings of the 23rd Annual International Conference on Mobile Systems, Applications and Services . New York : ACM , 2025 : 687 - 688 . DOI: 10.1145/3711875.3736680 http://dx.doi.org/10.1145/3711875.3736680 .

Wang Ying , Lei Jianjun , Shang Fengjun , et al . A comprehensive survey of multi-agent deep reinforcement learning for wireless spectrum management [J ] . Neurocomputing , 2025 , 653 : 131236 . DOI: 10.1016/j.neucom.2025.131236 http://dx.doi.org/10.1016/j.neucom.2025.131236 .

Zhang H , Zhou F , Wu Q , et al . Spectrum cognition: Semantic situation for next-generation spectrum management [J ] . IEEE Network , 2025 : 1 - 10 . DOI: 10.1109/MNET.2025.3604901 http://dx.doi.org/10.1109/MNET.2025.3604901 .

Voicu A M , Simić L , Petrova M . Survey of spectrum sharing for inter-technology coexistence [J ] . IEEE Communications Surveys & Tutorials , 2019 , 21 ( 2 ): 1112 - 1144 . DOI: 10.1109/comst.2018.2882308 http://dx.doi.org/10.1109/comst.2018.2882308

Kalmijn A J . The electric sense of sharks and rays [J ] . Journal of Experimental Biology , 1971 , 55 ( 2 ): 371 - 383 . DOI: 10.1242/jeb.55.2.371 http://dx.doi.org/10.1242/jeb.55.2.371 .

Hu Shengguo , Li Mingyi , Xu Jiawen , et al . Electromagnetic metamaterial agent [J ] . Light: Science & Applications , 2025 , 14 ( 1 ): 12 . DOI: 10.1038/s41377-024-01678-w http://dx.doi.org/10.1038/s41377-024-01678-w .

Altmann P , Schönberger J , Illium S , et al . Emergence in multi-agent systems: A safety perspective [C ] // Proceedings of 12th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation . Rigorous Engineering of Collective Adaptive Systems . Cham : Springer , 2024 : 104 - 120 . DOI: 10.1007/978-3-031-75107-3_7 http://dx.doi.org/10.1007/978-3-031-75107-3_7

Hlavacek D , Chang J M . A layered approach to cognitive radio network security: A survey [J ] . Computer Networks , 2014 , 75 : 414 - 436 . DOI: 10.1016/j.comnet.2014.10.001 http://dx.doi.org/10.1016/j.comnet.2014.10.001 .

Kailkhura B , Brahma S , Varshney P K . Consensus based detection in the presence of data falsification attacks [PP/OL ] . V1. arXiv ( 2015-04-14 )[ 2025-10-05 ] . https://arxiv.org/abs/1504.03413 https://arxiv.org/abs/1504.03413 . DOI: 10.1109/tsipn.2016.2607119 http://dx.doi.org/10.1109/tsipn.2016.2607119

Qadir J , Ahmed N , AHAD N . Building programmable wireless networks: An architectural survey [J ] . EURASIP Journal on Wireless Communications and Networking , 2014 , 2014 ( 1 ): 172 . DOI: 10.1186/1687-1499-2014-172 http://dx.doi.org/10.1186/1687-1499-2014-172 .

Iqbal A , Nauman A , Khurshaid T , et al . A scalable reinforcement learning framework for ultra-reliable low-latency spectrum management in healthcare internet of things [J ] . Mathematics , 2025 , 13 ( 18 ): 2941 . DOI: 10.3390/math13182941 http://dx.doi.org/10.3390/math13182941 .

Fraz M , Muslam M M A , Hussain M , et al . Smart sensing enabled dynamic spectrum management for cognitive radio networks [J ] . Frontiers in Computer Science , 2023 , 5 : 1271899 . DOI: 10.3389/fcomp.2023.1271899 http://dx.doi.org/10.3389/fcomp.2023.1271899 .

Chen Xu , Huang Jianwei . Distributed spectrum access with spatial reuse [J ] . IEEE Journal on Selected Areas in Communications , 2013 , 31 ( 3 ): 593 - 603 . DOI: 10.1109/JSAC.2013.130323 http://dx.doi.org/10.1109/JSAC.2013.130323 .

Zhang Yan , Zheng Jun , Chen H H , et al . Cognitive radio networks: Architectures, protocols, and standards [M ] . Boca Raton : CRC Press , 2010 . DOI: 10.1201/EBK1420077759 http://dx.doi.org/10.1201/EBK1420077759 .

Jha A , Gupta T , Rawat S S , et al . Strategic pseudo-goal perturbation for deadlock-free multi-agent navigation in social mini-games [C ] // 2024 9th International Conference on Control and Robotics Engineering (ICCRE) . Piscataway : IEEE , 2024 : 264 - 269 . DOI: 10.1109/iccre61448.2024.10589836 http://dx.doi.org/10.1109/iccre61448.2024.10589836

Bai Weiwei , Zheng Guoqiang , Xia Weibing , et al . Multi-user opportunistic spectrum access for cognitive radio networks based on multi-head self-attention and multi-agent deep reinforcement learning [J ] . Sensors , 2025 , 25 ( 7 ): 2025 . DOI: 10.3390/s25072025 http://dx.doi.org/10.3390/s25072025 .

Tan Xiang , Zhou Li , Wang Haijun , et al . Cooperative multi-agent reinforcement-learning-based distributed dynamic spectrum access in cognitive radio networks [J ] . IEEE Internet of Things Journal , 2022 , 9 ( 19 ): 19477 - 19488 . DOI: 10.1109/JIOT.2022.3168296 http://dx.doi.org/10.1109/JIOT.2022.3168296 .

Yu F R , Tang Helen , Huang Minyi , et al . Distributed consensus-based cooperative spectrum sensing in cognitive radio mobile ad hoc networks [M ] //Yu F R. Cognitive radio mobile ad hoc networks . New York : Springer , 2011 : 3 - 35 . DOI: 10.1007/978-1-4419-6172-3_1 http://dx.doi.org/10.1007/978-1-4419-6172-3_1

Patil A , Iyer S , López O L A , et al . A comprehensive survey on spectrum sharing techniques for 5G/B5G intelligent wireless networks: Opportunities, challenges and future research directions [J ] . Computer Networks , 2024 , 253 : 110697 . DOI: 10.1016/j.comnet.2024.110697 http://dx.doi.org/10.1016/j.comnet.2024.110697 .

Hernandez-Leal P , Kaisers M , Baarslag T , et al . A survey of learning in multiagent environments: Dealing with non-stationarity [EB/OL ] . ( 2019-03-11 )[ 2026-05-21 ] . https://doi.org/10.48550/arXiv.1707.09183 https://doi.org/10.48550/arXiv.1707.09183 .

Mitola III J . Cognitive radio architecture: The engineering foundations of radio XML [M ] . Hoboken : John Wiley & Sons, Inc. , 2006 . DOI: 10.1002/0471773735 http://dx.doi.org/10.1002/0471773735

Haykin S . Cognitive radar: A way of the future [J ] . IEEE Signal Processing Magazine , 2006 , 23 ( 1 ): 30 - 40 . DOI: 10.1109/MSP.2006.1593335 http://dx.doi.org/10.1109/MSP.2006.1593335 .

Akeela R , Dezfouli B . Software-defined radios: Architecture, state-of-the-art, and challenges [J ] . Computer Communications , 2018 , 128 : 106 - 125 . DOI: 10.1016/j.comcom.2018.07.012 http://dx.doi.org/10.1016/j.comcom.2018.07.012

Xu Yizhou , Xie Haidong , Ji Nan , et al . Dynamic adversarial jamming-based reinforcement learning for designing constellations [J ] . Digital Communications and Networks , 2024 , 10 ( 5 ): 1471 - 1479 . DOI: 10.1016/j.dcan.2023.05.012 http://dx.doi.org/10.1016/j.dcan.2023.05.012 .

Martin I C , Mukherjee S , Baimagambetov A , et al . Evolving machine learning: A survey [EB/OL ] . ( 2025-06-26 )[ 2026-05-21 ] . https://doi.org/10.48550/arXiv.2505.17902 https://doi.org/10.48550/arXiv.2505.17902 .

袁磊 , 张子谦 , 李立恒 , 等 . 开放环境下的协作多智能体强化学习进展 [J ] . 中国科学: 信息科学 , 2025 , 55 ( 2 ): 217 - 268 .

Yuan Lei , Zhang Ziqian , Li Liheng , et al . Progress on cooperative multi-agent reinforcement learning in open environment [J ] . Scientia Sinica Informationis , 2025 , 55 ( 2 ): 217 - 268 . (in Chinese)

Pfrommer S . Safety, robustness, and interpretability in machine learning [R/OL ] . Berkeley : University of California, Berkeley, ( 2025-05-15 )[ 2026-01-22 ] . https://www2.eecs.berkeley.edu/Pubs/TechRpts/2025/EECS-2025-67.html https://www2.eecs.berkeley.edu/Pubs/TechRpts/2025/EECS-2025-67.html .

Vouros G A . Explainable deep reinforcement learning: State of the art and challenges [J ] . ACM Computing Surveys , 2023 , 55 ( 5 ): 1 - 39 . DOI: 10.1145/3527448 http://dx.doi.org/10.1145/3527448 .

Kinney S L . Trusted platform module basics: Using TPM in embedded systems [M ] . Newton : Newnes , 2006 . DOI: 10.1016/b978-075067960-2/50014-2 http://dx.doi.org/10.1016/b978-075067960-2/50014-2

Ta D T . Channel surveillance strategy and interference reduction in future wireless networks [D/OL ] . Paris: Télécom ParisTech , 2018 [ 2026-05-21 ] . https://hal.science/tel-04563320 https://hal.science/tel-04563320 .

Molina-Tenorio Y , Prieto-Guerrero A , Rodriguez-Colina E , et al . Gramian angular field and convolutional neural networks for real-time multiband spectrum sensing in cognitive radio networks [J ] . Sensors , 2025 , 25 ( 12 ): 3580 . DOI: 10.3390/s25123580 http://dx.doi.org/10.3390/s25123580 .

Hu Junpeng , Zuo Zhen , Huang Zhiping , et al . Dynamic digital channelizer based on spectrum sensing [J ] . PLOS One , 2015 , 10 ( 8 ): e0136349 . DOI: 10.1371/journal.pone.0136349 http://dx.doi.org/10.1371/journal.pone.0136349 .

Harrison G , Sloan A , Myrick W , et al . Polyphase channelization utilizing general-purpose computing on a GPU [C ] // Proceedings of the SDR ’08 Technical Conference and Product Exposition . Washington, DC : SDR Forum, Inc. , 2008 .

Bhat A . Reconfigurable, cognitive software-defined radio [R ] . Washington, DC : NASA , 2015 .

NATO Science and Technology Organization . Cognitive radar [R ] . Brussels, Belgium : NATO Science and Technology Organization (STO) , 2020 .

崔国龙 , 余显祥 , 杨婧 , 等 . 认知雷达波形优化设计方法综述 [J ] . 雷达学报 , 2019 , 8 ( 5 ): 537 - 557 .

Cui Guolong , Yu Xianxiang , Yang Jing , et al . An overview of waveform optimization methods for cognitive radar [J ] . Journal of Radars , 2019 , 8 ( 5 ): 537 - 557 . (in Chinese)

肖宇 , 邓正宏 , 张展 . 基于双阶段互信息准则的多目标检测波形设计 [J ] . 系统工程与电子技术 , 2022 , 44 ( 9 ): 2736 - 2742 .

Xiao Yu , Deng Zhenghong , Zhang Zhan . Waveform design based on two-stage mutual information for multi-target detection [J ] . Systems Engineering and Electronics , 2022 , 44 ( 9 ): 2736 - 2742 . (in Chinese)

余若峰 , 杨威 , 付耀文 , 等 . 面向不同雷达任务的认知波形优化综述 [J ] . 电子学报 , 2022 , 50 ( 3 ): 726 - 752 . DOI: 10.12263/DZXB.20211068 http://dx.doi.org/10.12263/DZXB.20211068

Yu Ruofeng , Yang Wei , Fu Yaowen , et al . A review on cognitive waveform optimization for different radar missions [J ] . Acta Electronica Sinica , 2022 , 50 ( 3 ): 726 - 752 . (in Chinese) . DOI: 10.12263/DZXB.20211068 http://dx.doi.org/10.12263/DZXB.20211068

Ni Zhitong , Zhang J A , Yang Kai , et al . Waveform optimization with multiple performance metrics for broadband joint communication and radar sensing [EB/OL ] . ( 2020-11-22 )[ 2026-05-21 ] . https://doi.org/10.48550/arXiv.2011.10943 https://doi.org/10.48550/arXiv.2011.10943 .

Liu Yongjun , Liao Guisheng , Yang Zhiwei . Robust OFDM integrated radar and communications waveform design based on information theory [J ] . Signal Processing , 2019 , 162 : 317 - 329 . DOI: 10.1016/j.sigpro.2019.05.001 http://dx.doi.org/10.1016/j.sigpro.2019.05.001 .

王兴家 , 王彬 , 刘岳巍 , 等 . 基于元知识转移的认知雷达波形设计 [J ] . 雷达科学与技术 , 2024 , 22 ( 4 ): 443 - 453 .

Wang Xingjia , Wang Bin , Liu Yuewei , et al . Cognitive radar waveform design based on meta-knowledge transfer [J ] . Radar Science and Technology , 2024 , 22 ( 4 ): 443 - 453 . (in Chinese)

Wu Caihao , Huang Chuan , Li Yaowen , et al . Adaptive waveform design for cognitive radar target detection with constant modulus constraint [C ] // 2024 IEEE International Conference on Signal, Information and Data Processing (ICSIDP) . Piscataway : IEEE , 2024 : 1 - 5 . DOI: 10.1109/ICSIDP62679.2024.10868382 http://dx.doi.org/10.1109/ICSIDP62679.2024.10868382 .

Zhang Linke , Wei Na , Du Xuhao . Waveform design for improved detection of extended targets in sea clutter [J ] . Sensors , 2019 , 19 ( 18 ): 3957 . DOI: 10.3390/s19183957 http://dx.doi.org/10.3390/s19183957 .

Tang Bo , Li Jian . Spectrally constrained MIMO radar waveform design based on mutual information [J ] . IEEE Transactions on Signal Processing , 2019 , 67 ( 3 ): 821 - 834 . DOI: 10.1109/TSP.2018.2887186 http://dx.doi.org/10.1109/TSP.2018.2887186 .

Lu Ziyang , Kalia S , Gursoy M C , et al . Multi-objective reinforcement learning for cognitive radar resource management [C ] // ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . Piscataway : IEEE , 2025 : 1 - 5 . DOI: 10.1109/icassp49660.2025.10889885 http://dx.doi.org/10.1109/icassp49660.2025.10889885

Luo Junhai , He Xiaoting . A soft-hard combination decision fusion scheme for a clustered distributed detection system with multiple sensors [J ] . Sensors , 2018 , 18 ( 12 ): 4370 . DOI: 10.3390/s18124370 http://dx.doi.org/10.3390/s18124370 .

Fu Yuanhua , Yang Fan , He Zhiming . A quantization-based multibit data fusion scheme for cooperative spectrum sensing in cognitive radio networks [J ] . Sensors , 2018 , 18 ( 2 ): 473 . DOI: 10.3390/s18020473 http://dx.doi.org/10.3390/s18020473 .

Polo Y L , Wang Ying , Pandharipande A , et al . Compressive wide-band spectrum sensing [C ] // 2009 IEEE International Conference on Acoustics, Speech and Signal Processing . Piscataway : IEEE , 2009 : 2337 - 2340 . DOI: 10.1109/ICASSP.2009.4960089 http://dx.doi.org/10.1109/ICASSP.2009.4960089 .

Hamdaoui B , Khalfi B , GUIZANI M . Compressed wideband spectrum sensing: Concept, challenges, and enablers [J ] . IEEE Communications Magazine , 2018 , 56 ( 4 ): 136 - 141 . DOI: 10.1109/MCOM.2018.1700719 http://dx.doi.org/10.1109/MCOM.2018.1700719 .

Fang Jun , Wang Bin , Li Hongbin , et al . Recent advances on sub-nyquist sampling-based wideband spectrum sensing [J ] . IEEE Wireless Communications , 2021 , 28 ( 3 ): 115 - 121 . DOI: 10.1109/MWC.001.2000353 http://dx.doi.org/10.1109/MWC.001.2000353 .

Zhang Shunchao , Wang Yonghua , Wan Pin , et al . Clustering algorithm-based data fusion scheme for robust cooperative spectrum sensing [J ] . IEEE Access , 2020 , 8 : 5777 - 5786 . DOI: 10.1109/ACCESS.2019.2963512 http://dx.doi.org/10.1109/ACCESS.2019.2963512 .

Wu Jun , Liu Tianle , Zhao Rui . Beta distribution function for cooperative spectrum sensing against Byzantine attack in cognitive wireless sensor networks [J ] . Electronics , 2024 , 13 ( 17 ): 3386 . DOI: 10.3390/electronics13173386 http://dx.doi.org/10.3390/electronics13173386 .

刘彻 , 杨恺乔 , 鲍江涵 , 等 . 智能电磁计算的若干进展 [J ] . 雷达学报 , 2023 , 12 ( 4 ): 657 - 683 .

Liu Che , Yang Kaiqiao , Bao Jianghan , et al . Recent progress in intelligent electromagnetic computing [J ] . Journal of Radars , 2023 , 12 ( 4 ): 657 - 683 . (in Chinese)

王雪松 , 李健兵 , 徐丰 , 等 . 电磁空间信息资源的认知与利用 [J ] . 中国科学基金 , 2021 , 35 ( 5 ): 682 - 687 . DOI: 10.16262/j.cnki.1000-8217.2021.05.002 http://dx.doi.org/10.16262/j.cnki.1000-8217.2021.05.002 .

Wang Xuesong , Li Jianbing , Xu Feng , et al . Cognition and utilization of electromagnetic space information resources [J ] . Bulletin of National Natural Science Foundation of China , 2021 , 35 ( 5 ): 682 - 687 . DOI: 10.16262/j.cnki.1000-8217.2021.05.002. http://dx.doi.org/10.16262/j.cnki.1000-8217.2021.05.002. (in Chinese)

Jang S J , Han C H , Lee K E , et al . Reinforcement learning-based dynamic band and channel selection in cognitive radio ad-hoc networks [J ] . EURASIP Journal on Wireless Communications and Networking , 2019 , 2019 ( 1 ): 131 . DOI: 10.1186/s13638-019-1433-1 http://dx.doi.org/10.1186/s13638-019-1433-1 .

Ukpong U C , IDOWU-Bismark O , Adetiba E , et al . Deep reinforcement learning agents for dynamic spectrum access in television whitespace cognitive radio networks [J ] . Scientific African , 2025 , 27 : e02523 . DOI: 10.1016/j.sciaf.2024.e02523 http://dx.doi.org/10.1016/j.sciaf.2024.e02523 .

Zhou Shiyang , Cheng Yufan , Lei Xia , et al . Deep deterministic policy gradient with prioritized sampling for power control [J ] . IEEE Access , 2020 , 8 : 194240 - 194250 . DOI: 10.1109/ACCESS.2020.3033333 http://dx.doi.org/10.1109/ACCESS.2020.3033333 .

Nguyen C T , Van Huynh N , Chu N H , et al . Transfer learning for future wireless networks: A comprehensive survey [EB/OL ] . ( 2021-08-08 )[ 2026-05-21 ] . https://doi.org/10.48550/arXiv.2102.07572 https://doi.org/10.48550/arXiv.2102.07572 .

Zhou Ye , Ho H W . Online robot guidance and navigation in non-stationary environment with hybrid hierarchical reinforcement learning [J ] . Engineering Applications of Artificial Intelligence , 2022 , 114 : 105152 . DOI: 10.1016/j.engappai.2022.105152 http://dx.doi.org/10.1016/j.engappai.2022.105152 .

Bing Zhenshan , Lerch D , Huang Kai , et al . Meta-reinforcement learning in non-stationary and dynamic environments [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023 , 45 ( 3 ): 3476 - 3491 . DOI: 10.1109/TPAMI.2022.3185549 http://dx.doi.org/10.1109/TPAMI.2022.3185549 .

Pourshamsaei H , Nobakhti A . Predictive reinforcement learning in non-stationary environments using weighted mixture policy [J ] . Applied Soft Computing , 2024 , 153 : 111305 . DOI: 10.1016/j.asoc.2024.111305 http://dx.doi.org/10.1016/j.asoc.2024.111305 .

Padakandla S , K J P , Bhatnagar S . Reinforcement learning algorithm for non-stationary environments [J ] . Applied Intelligence , 2020 , 50 ( 11 ): 3590 - 3606 . DOI: 10.1007/s10489-020-01758-5 http://dx.doi.org/10.1007/s10489-020-01758-5 .

Luong N C , Hoang D T , Gong S , et al . Applications of deep reinforcement learning in communications and networking: A survey [J ] . IEEE Communications Surveys & Tutorials , 2019 , 21 ( 4 ): 3133 - 3174 . DOI: 10.1109/comst.2019.2916583 http://dx.doi.org/10.1109/comst.2019.2916583

Marden J R , Arslan G , Shamma J S . Cooperative control and potential games [J ] . IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) , 2009 , 39 ( 6 ): 1393 - 1407 . DOI: 10.1109/TSMCB.2009.2017273 http://dx.doi.org/10.1109/TSMCB.2009.2017273 .

Dasilva L , Bogucka H , Mackenzie A . Game theory in wireless networks [J ] . IEEE Communications Magazine , 2011 , 49 ( 8 ): 110 - 111 . DOI: 10.1109/MCOM.2011.5978423 http://dx.doi.org/10.1109/MCOM.2011.5978423 .

Wang Quan , Mao Zhendong , Wang Bin , et al . Knowledge graph embedding: A survey of approaches and applications [J ] . IEEE Transactions on Knowledge and Data Engineering , 2017 , 29 ( 12 ): 2724 - 2743 . DOI: 10.1109/TKDE.2017.2754499 http://dx.doi.org/10.1109/TKDE.2017.2754499 .

Yang Hailu , Zhang Jin , Luo Yang , et al . Representation learning on knowledge graph: A path attention-based method [J ] . Wireless Networks , 2026 , 32 ( 1 ): 15 - 30 . DOI: 10.1007/s11276-025-04041-y http://dx.doi.org/10.1007/s11276-025-04041-y .

Lin Yankai , Liu Zhiyuan , Sun Maosong , et al . Learning entity and relation embeddings for knowledge graph completion [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2015 , 29 ( 1 ): 2181 - 2187 . DOI: 10.1609/aaai.v29i1.9491 http://dx.doi.org/10.1609/aaai.v29i1.9491 .

Yang Yaodong , Luo Rui , Li Minne , et al . Mean field multi-agent reinforcement learning [C ] // Proceedings of the 35th International Conference on Machine Learning . St . John’s, Canada : JMLR Workshop and Conference Proceedings , 2018 : 5567 - 5576 .

陈平平 , 张旭 , 谢肇鹏 , 等 . 基于多智能体近端策略优化的多信道动态频谱接入 [J ] . 电子学报 , 2024 , 52 ( 6 ): 1824 - 1831 .

Chen Pingping , Zhang Xu , Xie Zhaopeng , et al . Multi-channel dynamic spectrum access based on multi-agent proximal policy optimization [J ] . Acta Electronica Sinica , 2024 , 52 ( 6 ): 1824 - 1831 . (in Chinese)

OLFATI-Saber R , Fax J A , Murray R M . Consensus and cooperation in networked multi-agent systems [J ] . Proceedings of the IEEE , 2007 , 95 ( 1 ): 215 - 233 . DOI: 10.1109/JPROC.2006.887293 http://dx.doi.org/10.1109/JPROC.2006.887293 .

Yan Zijiang , Zhou Hao , Pei Jianhua , et al . Hierarchical and collaborative LLM-based control for multi-UAV motion and communication in integrated terrestrial and non-terrestrial networks [PP/OL ] .( 2025-06-06 )[ 2026-05-21 ] . https://doi.org/10.48550/arXiv.2506.06532 https://doi.org/10.48550/arXiv.2506.06532 .

Jokela T , Kokkinen H , Kalliovaara J , et al . Trial of spectrum sharing in 2.3GHz band for two types of PMSE equipment and mobile network [C ] // 2018 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB) . Piscataway : IEEE , 2018 : 1 - 5 . DOI: 10.1109/BMSB.2018.8436685 http://dx.doi.org/10.1109/BMSB.2018.8436685 .

Xu Yuhua , Wang Jinlong , Wu Qihui , et al . Dynamic spectrum access in time-varying environment: Distributed learning beyond expectation optimization [J ] . IEEE Transactions on Communications , 2017 , 65 ( 12 ): 5305 - 5318 . DOI: 10.1109/tcomm.2017.2734768 http://dx.doi.org/10.1109/tcomm.2017.2734768

Zhou Liang , Chen Mingwei . Optimizing collaborative beamforming strategies for energy-efficient wireless sensor networks in large-scale IoT deployments [J ] . Nuvern Applied Science Reviews , 2025 , 9 ( 1 ): 1 - 14 .

López D , Rivas E , Gualdron O . Primary user characterization for cognitive radio wireless networks using a neural system based on Deep Learning [J ] . Artificial Intelligence Review , 2019 , 52 ( 1 ): 169 - 195 . DOI: 10.1007/s10462-017-9600-4 http://dx.doi.org/10.1007/s10462-017-9600-4 .

许瑞琛 , 蒋挺 . 基于POMDP的认知无线电自适应频谱感知算法 [J ] . 通信学报 , 2013 , 34 ( 6 ): 49 - 56 .

Xu Ruichen , Jiang Ting . Cognitive radio auto-adaptive sensing algorithm based on POMDP [J ] . Journal on Communications , 2013 , 34 ( 6 ): 49 - 56 . (in Chinese)

Zhang Rui , Gao Feifei , Liang Yingchang . Cognitive beamforming made practical: Effective interference channel and learning-throughput tradeoff [J ] . IEEE Transactions on Communications , 2010 , 58 ( 2 ): 706 - 718 . DOI: 10.1109/tcomm.2010.02.080476 http://dx.doi.org/10.1109/tcomm.2010.02.080476

Lee H W , Chang W , Jung B C . Optimal power allocation and allowable interference shaping in cognitive radio networks [J ] . Computers & Electrical Engineering , 2018 , 71 : 265 - 272 . DOI: 10.1016/j.compeleceng.2018.07.015 http://dx.doi.org/10.1016/j.compeleceng.2018.07.015 .

Jagannath J , Furman S , Melodia T , et al . Design and experimental evaluation of a cross-layer deadline-based joint routing and spectrum allocation algorithm [J ] . IEEE Transactions on Mobile Computing , 2019 , 18 ( 8 ): 1774 - 1788 . DOI: 10.1109/TMC.2018.2866093 http://dx.doi.org/10.1109/TMC.2018.2866093 .

李剑锋 , 代健 , 郝新红 , 等 . 无线电引信认知抗干扰模型及关键技术综述 [J ] . 探测与控制学报 , 2022 , 44 ( 5 ): 1 - 9 .

Li Jianfeng , Dai Jian , Hao Xinhong , et al . Review of cognitive anti-jamming model and key technology of Radio Fuze [J ] . Journal of Detection & Control , 2022 , 44 ( 5 ): 1 - 9 . (in Chinese)

Simon M K , Omura J K , Scholtz R A , et al . Spread spectrum communications handbook [M ] . New York : McGraw-Hill , 1994 .

Li Rui , Xu Le . Application of computational electromagnetics techniques and artificial intelligence in the engineering [J ] . Electronics , 2024 , 13 ( 10 ): 1835 . DOI: 10.3390/electronics13101835 http://dx.doi.org/10.3390/electronics13101835 .

Zarka N , Khalil A , Assimi A . Adaptive modulation and coding simulations for mobile communication networks [C ] // Proceedings of the International Conference for Young Researchers in Informatics and Mathematics (ICYRIME 2016) . Damascus : Higher Institute for Applied Sciences and Technology , 2016 : 36 - 40 .

Renfors M , Siohan P , Farhang-Boroujeny B , et al . Filter banks for next generation multicarrier wireless communications [J ] . EURASIP Journal on Advances in Signal Processing , 2010 , 2010 ( 1 ): 314193 . DOI: 10.1155/2010/314193 http://dx.doi.org/10.1155/2010/314193 .

Nadkar T , Thumar V , Tej G P S , et al . Adaptive guard interval and power allocation for OFDM-based cognitive radio [J ] . ICTACT Journal on Communication Technology , 2011 , 2 ( 2 ): 314 - 322 . DOI: 10.21917/IJCT.2011.0044 http://dx.doi.org/10.21917/IJCT.2011.0044 .

杨国 , 施鸿强 , 黎小聪 , 等 . 大频率比的毫米波频率可重构滤波天线 [J ] . 物理学报 , 2025 , 74 ( 1 ): 018401 . DOI: 10.7498/aps.74.20241494 http://dx.doi.org/10.7498/aps.74.20241494

Yang Guo , Shi Hongqiang , Li Xiaocong , et al . Millimeter-wave frequency-reconfigurable filtering antenna with high frequency turning ratio [J ] . Acta Physica Sinica , 2025 , 74 ( 1 ): 018401 . (in Chinese) . DOI: 10.7498/aps.74.20241494 http://dx.doi.org/10.7498/aps.74.20241494

Shakibafar B , Farhangian F , Gagne J M , et al . An adaptive RF front-end architecture for multi-band SDR in avionics [J ] . Sensors , 2024 , 24 ( 18 ): 5963 . DOI: 10.3390/s24185963 http://dx.doi.org/10.3390/s24185963 .

Zhu Lipeng , Ma Wenyan , Zhang Rui . Movable-antenna array enhanced beamforming: Achieving full array gain with null steering [J ] . IEEE Communications Letters , 2023 , 27 ( 12 ): 3340 - 3344 . DOI: 10.1109/LCOMM.2023.3323656 http://dx.doi.org/10.1109/LCOMM.2023.3323656 .

Murshed R U , Ullah M S , Saquib M , et al . Self-supervised contrastive learning for 6G UM-MIMO THz communications: Improving robustness under imperfect CSI [PP/OL ] . V2. arXiv ( 2024-01-21 )[ 2025-10-11 ] . https://arxiv.org/abs/2401.11376 https://arxiv.org/abs/2401.11376 . DOI: 10.1109/iccworkshops59551.2024.10615313 http://dx.doi.org/10.1109/iccworkshops59551.2024.10615313

Li Oupeng , He Jia , Zeng Kun , et al . Integrated sensing and communication in 6G: A prototype of high resolution multichannel THz sensing on portable device [J ] . EURASIP Journal on Wireless Communications and Networking , 2022 , 2022 ( 1 ): 106 . DOI: 10.1186/s13638-022-02172-w http://dx.doi.org/10.1186/s13638-022-02172-w .

Yang Jun , Qin Junxiang , Guo Xiye , et al . Open and shared sustainable mega-constellation [J ] . National Science Review , 2025 , 12 ( 11 ): nwaf344 . DOI: 10.1093/nsr/nwaf344 http://dx.doi.org/10.1093/nsr/nwaf344 .

Wang Luting , Xiang Yinghao , Huang Hongliang , et al . Towards realistic earth-observation constellation scheduling: Benchmark and methodology [PP/OL ] . V1. arXiv ( 2025-10-30 )[ 2025-12-24 ] . https://arxiv.org/abs/2510.26297 https://arxiv.org/abs/2510.26297 .

Joint Chiefs of Staff . Joint electromagnetic spectrum operations (JP 3-85) [R ] . Washington, DC : Joint Chiefs of Staff , 2020-05-22 .

U.S. Department of Defense . Electromagnetic spectrum superiority strategy [R ] . Washington, DC : U.S. Department of Defense , 2020-10-29 .

张春磊 , 裴琴 , 易楷翔 . 美电磁频谱作战技术体系与应对策略研究 [J ] . 中国电子科学研究院学报 , 2022 , 17 ( 5 ): 439 - 444 .

Zhang Chunlei , Pei Qin , Yi Kaixiang . Study on the technological architecture of U.S. electromagnetic spectrum operations and countermeasures [J ] . Journal of China Academy of Electronics and Information Technology , 2022 , 17 ( 5 ): 439 - 444 . (in Chinese)

Chen Yihui , Yang Helin , Ou Xiaoyu , et al . Anti-jamming resource allocation for integrated sensing and communications based on game-guided reinforcement learning [J ] . IEEE Wireless Communications Letters , 2025 , 14 ( 1 ): 223 - 227 . DOI: 10.1109/LWC.2024.3496437 http://dx.doi.org/10.1109/LWC.2024.3496437 .

王沙飞 , 鲍雁飞 , 李岩 . 认知电子战体系结构与技术 [J ] . 中国科学: 信息科学 , 2018 , 48 ( 12 ): 1603 - 1613 . DOI: 10.1360/n112018-00153 http://dx.doi.org/10.1360/n112018-00153

Wang Shafei , Bao Yanfei , Li Yan . The architecture and technology of cognitive electronic warfare [J ] . Scientia Sinica Informationis , 2018 , 48 ( 12 ): 1603 - 1613 . (in Chinese) . DOI: 10.1360/n112018-00153 http://dx.doi.org/10.1360/n112018-00153

黄知涛 , 王翔 , 赵雨睿 . 认知电子战综述 [J ] . 国防科技大学学报 , 2023 , 45 ( 5 ): 1 - 11 .

Huang Zhitao , Wang Xiang , Zhao Yurui . Overview of cognitive electronic warfare [J ] . Journal of National University of Defense Technology , 2023 , 45 ( 5 ): 1 - 11 . (in Chinese)

陈小瑜 . 认知雷达及其对抗技术浅析 [J ] . 新一代信息技术 , 2022 , 5 ( 9 ): 12 - 17 .

Chen Xiaoyu . An analysis of cognitive radar and its countermeasure [J ] . New Generation of Information Technology , 2022 , 5 ( 9 ): 12 - 17 . (in Chinese)

Wu K S , Wang L , Chen Y J , eds . Edge computing and IoT: Systems, management and security: Proceedings of the 2nd EAI International Conference, ICECI 2021 [C ] . Cham : Springer International Publishing , 2022 . https://doi.org/10.1007/978-3-031-04231-7 https://doi.org/10.1007/978-3-031-04231-7 .

Zhen Pan , Zhu Bowen , Wang Ning , et al . Electromagnetic twin space: When digital twins meet the electromagnetic space [J ] . Electronics , 2025 , 14 ( 22 ): 4546 . DOI: 10.3390/electronics14224546 http://dx.doi.org/10.3390/electronics14224546 .

Jiang Shuaifeng , Qu Qi , Pan Xiaqing , et al . Learnable wireless digital twins: Reconstructing electromagnetic field with neural representations [J ] . IEEE Open Journal of the Communications Society , 2025 , 6 : 1568 - 1590 . DOI: 10.1109/OJCOMS.2025.3535959 http://dx.doi.org/10.1109/OJCOMS.2025.3535959 .

Jiang Feibo , Pan Cunhua , Dong Li , et al . A comprehensive survey of large AI models for future communications: Foundations, applications, and challenges [J ] . IEEE Communications Surveys & Tutorials , 2026 , 28 : 4731 - 4764 . DOI: 10.1109/COMST.2026.3660844 http://dx.doi.org/10.1109/COMST.2026.3660844 .

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Related Author

WANG Jingyao

SONG Jian

GUO Jinghua

ZENG Zeqin

XU Xinlong

HUANG Xia

SHI Qingyu

WANG Zhen

Related Institution

Department of Automation, Xiamen University

College of Electrical Engineering and Automation, Shandong University of Science and Technology

College of Mathematics and Systems Sciences, Shandong University of Science and Technology

School of Automation Science and Electrical Engineering, Beihang University

Zhongguancun Laboratory

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