复杂电磁环境下频谱智能管控技术探讨

doi:10.7527/S1000-6893.2020.24750

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复杂电磁环境下频谱智能管控技术探讨

丁国如, 孙佳琛, 王海超, 焦雨涛

陆军工程大学通信工程学院, 南京 210007

收稿日期:2020-09-14 修回日期:2020-10-09 发布日期:2020-12-25
通讯作者: 丁国如 E-mail:dr.guoru.ding@ieee.org
基金资助:
国家自然科学基金（U20B2038，61871398，61931011）；江苏省自然科学基金杰出青年项目（BK20190030）；科技创新2030—"新一代人工智能"重大项目（2018AAA0102303）

Discussion on technologies for intelligent spectrum management and control under complex electromagnetic environments

DING Guoru, SUN Jiachen, WANG Haichao, JIAO Yutao

College of Communications Engineering, Army Engineering University, Nanjing 210007, China

Received:2020-09-14 Revised:2020-10-09 Published:2020-12-25
Supported by:
National Natural Science Foundation of China (U20B2038, 61871398, 61931011); Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province (BK20190030); Science and Technology Innovation 2030-Key Project of "New Generation Artificial Intelligence" of China (2018AAA0102303)

摘要/Abstract

摘要： 作为国土空间的重要组成，电磁频谱空间呈现出环境错综复杂、目标类型多样、用频行为多变等新挑战，导致频谱安全问题日益严峻。面向复杂电磁环境下频谱秩序安全、频谱对抗安全和频谱共享安全等需求，基于人工智能的频谱管控成为电磁频谱领域的重要研究方向，存在着具有挑战性的基础理论问题和关键技术难题。首先，调研了复杂电磁环境下频谱智能管控的国家战略需求，然后，提炼了智能频谱管控的科学意义与技术挑战，进一步从频谱管控模型机理、频谱态势感知、频谱行为推理、频谱安全决策和频谱管控应用系统等5个方面梳理国内外研究现状，并分析了相关发展动态及研究趋势。

关键词: 复杂电磁环境, 频谱管控, 人工智能, 信息流, 无人机群, 物联网

Abstract: As an important component of national land space, the electromagnetic spectrum space presents many new challenges, such as the intricacy and complexity of the electromagnetic environment, diversity of electromagnetic targets and variability of spectrum usage behaviors. As a result, spectrum security becomes an increasingly urgent issue. For the common national defense requirements of security for spectrum order, security for spectrum confrontation and security for spectrum sharing in the complex electromagnetic environment, spectrum management and control based on artificial intelligence has become the most important research orientation in the radio spectrum field. There exist the challenging fundamental theoretical and technical problems in this research field. This paper firstly investigates the national strategic demands for intelligent spectrum management and control under complex electromagnetic environments, and then summarizes the significance and technical challenges of intelligent spectrum management and control. The present domestic and foreign research on five aspects are summarized, including the model and mechanism of spectrum management, spectrum sensing, spectrum inference, spectrum safety decision and typical application systems. Related developments and trends are analyzed.

Key words: complex electromagnetic environment, spectrum management and control, artificial intelligence, information flow, unmanned aerial vehicle swarm, internet of things

中图分类号:

V234.1

丁国如, 孙佳琛, 王海超, 焦雨涛. 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): 524750-524750.

DING Guoru, SUN Jiachen, WANG Haichao, JIAO Yutao. Discussion on technologies for intelligent spectrum management and control under complex electromagnetic environments[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(4): 524750-524750.

参考文献

[1] HOLLAND J H. Signals and boundaries:Building blocks for complex adaptive systems[M]. Cambridge:MIT Press, 2014.
[2] 姚富强, 张余, 柳永祥. 电磁频谱安全与控制[J]. 指挥与控制学报, 2015, 1(3):278-283. YAO F Q, ZHANG Y, LIU Y X. Security and control for electromagnetic spectrum[J]. Journal of Command and Control, 2015, 1(3):278-283(in Chinese).
[3] LIANG Y C. Dynamic spectrum management:From cognitive radio to blockchain and artificial intelligence[M]. Singapore:Springer Singapore, 2020.
[4] CLARK B, GUNZINGER M. Winning the airwaves:Regaining America's dominance in the electromagnetic spectrum[R]. Center of Strategic and Budgetary Assessments (CSBA), 2015.
[5] CLARK B, GUNZINGER M, SLOMAN J. Winning in the gray zone:Using electromagnetic warfare to regain escalation dominance[R]. Center of Strategic and Budgetary Assessments (CSBA), 2017.
[6] CLARK B, MCNAMARA M W, WALTON A T. Winning the invisible war:Gaining an enduring U.S. advantage in the electromagnetic spectrum[R]. Center of Strategic and Budgetary Assessments (CSBA), 2019.
[7] 冯志勇, 张平, 郎保真, 等. 认知无线网络理论与关键技术[M]. 北京:人民邮电出版社, 2011. FENG Z Y, ZHANG P, LANG B Z, et al. Cognitive wireless network theory and key technology[M]. Beijing:Posts & Telecom Press, 2011(in Chinese).
[8] SHENG M, LI X, WANG X, et al. Topology control with successive interference cancellation in cognitive radio networks[J]. IEEE Transactions on Communications, 2017, 65(1):37-48.
[9] YIN L G, JIANG C N, JIANG C J, et al. Collaborative spectrum managements and sharing in coordinated space, terrestrial and ocean networks[J]. IEEE Network, 2020, 34(1):182-187.
[10] TARVER C, TONNEMACHER M, CHANDRASEKH-AR V, et al. Enabling a "Use-or-Share" framework for PAL-GAA sharing in CBRS networks via reinforcement learning[J]. IEEE Transactions on Cognitive Communications and Networking, 2019, 5(3):716-729.
[11] JOHN L. Commercial deployment for Huawei's 4G/5G flash dynamic spectrum sharing[EB/OL]. (2020-02-21)[2020-09-10]. https://www.accesswire.com/577361/Commercial-Deployment-for-Huaweis-4G5G-Flash-Dynamic-Spectrum-Sharing.
[12] 工业和信息化部.中华人民共和国无线电频率划分规定[M].北京:人民邮电出版社,2014. Ministry of Industry and Information Technology. People's republic of china regulations on radio allocation[M]. Beijing:Posts & Telecom Press, 2014(in Chinese).
[13] CAVE M, DOYLE C, WEBB W. Essentials of modern spectrum management[M]. Cambridge:Cambridge University Press, 2007.
[14] 王沙飞.人工智能与电磁频谱战[N].中国信息化周报,2018-05-14(16). WANG S F. Artificial intelligence and electromagnetic spectrum warfare[N]. China Information Weekly, 2018-05-14(16) (in Chinese).
[15] KELLY H. DARPA's spectrum collaboration challenge:Hulking out, helping out and exploring the limits of ai-based spectrum sharing[EB/OL]. (2019-10-24)[2020-09-10].https://www.rcrwireless.com/20191024/spectrum/darpas-spectrum-collaboration-challenge-hulking-out-helping-out-exploring-limits-of-ai-spectrum-management?utm_campaign=20191024%20RCRenewsThurs&utm_medium=email&utm_source=Eloqua.
[16] NTIA Office. Spectrum management[EB/OL].[2020-09-13]. https://www.ntia.doc.gov/legacy/osmhome/training/training.html.
[17] DEFENSE U. Coalition Joint Spectrum Management and Planning Tool (CJSMPT)[EB/OL]. (2006-04-01)[2020-09-10]. https://defense-update.com/20060401_coalition-joint-spectrum-management-and-planning-tool-cjsmpt.html.
[18] Global Electromagnetic Spectrum Information System (GEMSIS)[EB/OL].[2020-09-13].https://www.disa.mil/~/media/Files/DISA/Services/DSO/GEMSISSpectrumCapabilities.pdf.
[19] 吴启晖, 丁国如, 孙佳琛. 电磁频谱数据挖掘理论与应用[M]. 北京:科学出版社, 2020. WU Q H, DING G R, SUN J C. Electromagnetic spectrum data mining theories and applications[M]. Beijing:Science Press, 2020(in Chinese).
[20] DING G, WU Q, ZHANG L, et al. An amateur drone surveillance system based on the cognitive Internet of Things[J]. IEEE Communications Magazine, 2018, 56(1):29-35.
[21] CABRIC D S, MISHRA S M, BRODERSEN R W. Implementation issues in spectrum sensing for cognitive radios[C]//Conference on Signals, Systems & Computers. Piscataway:IEEE Press, 2004:772-776.
[22] DING G, WANG J, WU Q, et al. Robust spectrum sensing with crowd sensors[J]. IEEE Transactions on Communications, 2014, 62(9):3129-3143.
[23] YUCEK T, ARSLAN H. A survey of spectrum sensing algorithms for cognitive radio applications[J]. IEEE Communications Surveys and Tutorials, 2009, 11(1):116-130.
[24] DING G, WU F, WU Q, et al. Robust online spectrum prediction with incomplete and corrupted historical observations[J]. IEEE Transactions on Vehicular Technology, 2017, 66(9):8022-8036.
[25] 任峰. 频谱态势感知数据挖掘技术研究[D]. 北京:北京邮电大学, 2015. REN F. Data mining technology of spectrum sensing data[D]. Beijing:Beijing University of Posts and Telecommunications, 2015(in Chinese).
[26] SUN J, WANG J, CHEN J, et al. Clustering analysis for internet of spectrum devices:Real-world data analytics and applications[J]. IEEE Internet of Things Journal, 2020, 7(5):4485-4496.
[27] 陈敏. 基于无线电监测的电磁目标时空信息挖掘[D].成都:电子科技大学, 2019. CHEN M. Spatio-temporal information mining of electromagnetic targets based on radio monitoring[D]. Chengdu:University of Electronic Science and Technology of China, 2019(in Chinese).
[28] 张莉. 频谱数据挖掘算法研究与应用[D]. 北京:北京邮电大学, 2016. ZHANG L. Data mining algorithm of spectrum sensing data[D]. Beijing:Beijing University of Posts and Telecommunications, 2016(in Chinese).
[29] LIU X, SHI R, HEE B, et al. Detection on abnormal usage of spectrum by electromagnetic data mining[C]//2019 IEEE 4th International Conference on Big Data Analytics (ICBDA). Piscataway:IEEE Press, 2019.
[30] GIANNAKIS G B, SHEN Y, KARANIKOLAS G V. Topology identification and learning over graphs:Accounting for nonlinearities and dynamics[J]. Proceedings of the IEEE, 2018, 106(5):787-807.
[31] MOORE M G, DAVENPORT M A. Analysis of wireless networks using Hawkes processes[C]//2016 IEEE 17th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). Piscataway:IEEE Press, 2016:1-5.
[32] LAGHATE M, CABRIC D. Learning wireless networks' topologies using asymmetric granger causality[J]. IEEE Journal of Selected Topics in Signal Processing, 2018, 12(1):233-247.
[33] DING G, JIAO Y, WANG J, et al. Spectrum inference in cognitive radio networks:Algorithms and applications[J]. IEEE Communications Surveys and Tutorials, 2018, 20(1):150-182.
[34] 李彤, 苗成林, 吕军, 等. 频谱用户行为特征建模与分析[J]. 装甲兵工程学院学报, 2019, 33(1):87-92. LI T, MIAO C L, LÜ J, et al. Research on modeling of spectrum behavior characteristics[J]. Journal of Academy of Armored Force Engineering, 2019, 33(1):87-92(in Chinese).
[35] WILLKOMM D, MACHIRAJU S, BOLOT J, et al. Primary user behavior in cellular networks and implications for dynamic spectrum access[J]. IEEE Communications Magazine, 2009, 47(3):88-95.
[36] DING G, WANG J, WU Q, et al. On the limits of predictability in real-world radio spectrum state dynamics:From entropy theory to 5G spectrum sharing[J]. IEEE Communications Magazine, 2015, 53(7):178-183.
[37] XING X, JING T, CHENG W, et al. Spectrum prediction in cognitive radio networks[J]. IEEE Wireless Communications, 2013, 20(2):90-96.
[38] LI H. Reconstructing spectrum occupancies for wideband cognitive radio networks:A matrix completion via belief propagation[C]//International Conference on Communications, 2010:1-6.
[39] YIN S, CHEN D, ZHANG Q, et al. Mining spectrum usage data:A large-scale spectrum measurement study[J]. IEEE Transactions on Mobile Computing, 2012, 11(6):1033-1046.
[40] KIM S J, GIANNAKIS G B. Cognitive radio spectrum prediction using dictionary learning[C]//Global Communications Conference. Piscataway:IEEE Press, 2013:3206-3211.
[41] YU L, CHEN J, DING G. Spectrum prediction via long short term memory[C]//2017 3rd IEEE International Conference on Computer and Communications (ICCC). Piscataway:IEEE Press, 2017.
[42] YU L, WANG Q, GUO Y, et al. Spectrum availability prediction in cognitive aerospace communications:A deep learning perspective[C]//2017 Cognitive Communications for Aerospace Applications Workshop (CCAA), 2017:1-4.
[43] WANG J, TANG J, XU Z, et al. Spatiotemporal modeling and prediction in cellular networks:A big data enabled deep learning approach[C]//IEEE INFOCOM 2017-IEEE Conference on Computer Communications. Piscataway:IEEE Press, 2017:1-9.
[44] YU L, CHEN J, ZHANG Y, et al. Deep spectrum prediction in high frequency communication based on temporal-spectral residual network[J]. China Communications, 2018, 15(9):25-34.
[45] ZHUANG F, QI Z, DUAN K, et al. A comprehensive survey on transfer learning[J]. Proceedings of the IEEE, 2021, 109(1):43-76.
[46] LIN F, CHEN J, SUN J, et al. Cross-band spectrum prediction based on deep transfer learning[J]. China Communications, 2020, 17(2):66-80.
[47] MASONTA M T, MZYECE M, NTLATLAPA N, et al. Spectrum decision in cognitive radio networks:A survey[J]. IEEE Communications Surveys and Tutorials, 2013, 15(3):1088-1107.
[48] LI Z, GUAN L, LI C, et al. A secure intelligent spectrum control strategy for future THz mobile heterogeneous networks[J]. IEEE Communications Magazine, 2018, 56(6):116-123.
[49] LAUGHLIN C. Spectrum vulnerabilities, Part I:Systematic and technological challenges to identifying and understanding vulnerabilities[J]. the Internet of Things, 2019, 2(4):6-7.
[50] PELECHRINIS K, ILIOFOTOU M, KRISHNAMURT-HY S V, et al. Denial of service attacks in wireless networks:The case of jammers[J]. IEEE Communications Surveys and Tutorials, 2011, 13(2):245-257.
[51] THERESA S, KULKARNI J A A. Shannon meets von Neumann:A minimax theorem for channel coding in the presence of a jammer[J]. IEEE Transactions on Information Theory, 2020, 66(5):2842-2859.
[52] 姚富强. 通信抗干扰工程与实践[M].第2版. 北京:电子工业出版社,2012. YAO F Q. Communication anti-jamming engineering and practice[M]. 2nd.ed. Beijing:Publishing House of Electronics Industry, 2012(in Chinese).
[53] 张春磊,杨小牛. 认知电子战与认知电子战系统研究[J]. 中国电子科学研究院学报,2014,9(6):551-555. ZHANG C L, YANG X N. Research on the cognitive electronic warfare and cognitive electronic warfare system[J]. Journal of China Academy of Electronics and Information Technology, 2014, 9(6):551-555(in Chinese).
[54] 李少谦, 程郁凡, 董彬虹,等. 智能抗干扰通信技术研究[J]. 无线电通信技术, 2012, 38(1):5-8. LI S Q, CHENG Y F, DONG B H, et al. Research on intelligent anti-jam communication techniques[J]. Radio Communications Technology, 2012, 38(1):5-8(in Chinese).
[55] YANG D, XUE G, ZHANG J, et al. Coping with a smart jammer in wireless networks:A stackelberg game approach[J]. IEEE Transactions on Wireless Communications, 2013, 12(8):4038-4047.
[56] XIAO L, LI Y, DAI C, et al. Reinforcement learning-based NOMA power allocation in the presence of smart jamming[J]. IEEE Transactions on Vehicular Technology, 2018, 67(4):3377-3389.
[57] HAN G, XIAO L, POOR H V, et al. Two-dimensional anti-jamming communication based on deep reinforcement learning[C]//2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Piscataway:IEEE Press, 2017:2087-2091.
[58] YAO F, JIA L. A collaborative multi-agent reinforcement learning anti-jamming algorithm in wireless networks[J]. IEEE Wireless Communications Letters, 2019, 8(4):1024-1027.
[59] FCC's Rosenworcel talks up 6G?[EB/OL].[2020-09-13]. https://www.multichan-nel.com/news/fccsrosenworcel-talks-up-6 g.
[60] KOTOBI K, BILEN S G. Secure blockchains for dynamic spectrum access:A decentralized database in moving cognitive radio networks enhances security and user access[J]. IEEE Vehicular Technology Magazine, 2018, 13(1):32-39.
[61] BAYHAN S, ZUBOW A, GAWLOWICZ P, et al. Smart contracts for spectrum sensing as a service[J]. IEEE Transactions on Cognitive Communications and Networking, 2019, 5(3):648-660.
[62] JIAO Y, WANG P, NIYATO D, et al. Auction mechanisms in cloud/fog computing resource allocation for public blockchain networks[J]. IEEE Transactions on Parallel and Distributed Systems, 2019, 30(9):1975-1989.
[63] 杨健, 陈曦, 丁国如, 等. 基于区块链的频谱设备网络中防御拜占庭攻击的分布式共识机制[J]. 通信学报, 2020, 41(3):1-16. YANG J, CHEN X, DING G R, et al. Blockchain-driven distributed consensus mechanism in defensing Byzantine attack for the Internet of spectrum device[J].Journal on Communications, 2020, 41(3):1-16(in Chinese).
[64] QIU J, GRACE D, DING G, et al. Blockchain-based secure spectrum trading for unmanned-aerial-vehicle-assisted cellular networks:An operator's perspective[J]. IEEE Internet of Things Journal, 2020, 7(1):451-466.
[65] BASH B A, GOECKEL D, TOWSLEY D, et al. Hiding information in noise:Fundamental limits of covert wireless communication[J]. IEEE Communications Magazine, 2015, 53(12):26-31.
[66] LIU Z, LIU J, ZENG Y, et al. Covert wireless communications in IoT systems:Hiding information in interference[J]. IEEE Wireless Communications, 2018, 25(6):46-52.
[67] BASH B A, GOECKEL D, TOWSLEY D, et al. Limits of reliable communication with low probability of detection on AWGN channels[J]. IEEE Journal on Selected Areas in Communications, 2013, 31(9):1921-1930.
[68] SHAHZAD K, ZHOU X, YAN S, et al. Achieving covert wireless communications using a full-duplex receiver[J]. IEEE Transactions on Wireless Communications, 2018, 17(12):8517-8530.
[69] ZHENG T, WANG H, NG D W, et al. Multi-antenna covert communications in random wireless networks[J]. IEEE Transactions on Wireless Communications, 2019, 18(3):1974-1987.
[70] COTTON S L, SCANLON W, MADAHAR B, et al. Millimeter-wave soldier-to-soldier communications for covert battlefield operations[J]. IEEE Communications Magazine, 2009, 47(10):72-81.
[71] YAN S, ZHOU X, HU J, et al. Low probability of detection communication:Opportunities and challenges[J]. IEEE Wireless Communications, 2019, 26(5):19-25.

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