Communication Topology Inference Technology for Non-cooperative UAV Communication Network
-
摘要: 在对抗环境下，捕获无人机通信网络的通联拓扑有助于我们高效发现并破坏其集群功能。然而，在非合作条件下，传统的拓扑先验信息难以获取，通联拓扑推理面临着巨大的挑战。现有相关研究总体上仍处于起步阶段，系统模型和推理机理不清晰，各类方法在同一数据维度下的对比较少。因此，针对非合作的物理场景，该文首先构建了系统模型，揭示了推理机理。然后，分别对相关性、格兰杰因果、转移熵和多维霍克斯过程4种方法进行了仿真对比分析。最后，对该研究方向的发展前景进行了展望。Abstract: In confrontational environment, capturing the communication topology of the Unmanned Aerial Vehicles (UAV) communication network helps us to discover efficiently and destroy its cluster function. However, under non-cooperative conditions, the traditional priori information of topology is difficult to obtain, and communication topology inference faces huge challenges. Existing related research is still in its infancy as a whole, the system model and inference mechanism are not clear, and the comparison of various methods in the same data dimension is also rare. Therefore, for the non-cooperative physical scene, firstly the system model is constructed and the inference mechanism is revealed. Then, the four methods of correlation, Granger causality, transfer entropy and multidimensional Hawkes process are simulated and compared. Finally, the prospects for the development of this research direction are prospected.
-
[1] FENG Wanmei, TANG Jie, ZHAO Nan, et al. NOMA-based UAV-aided networks for emergency communications[J]. China Communications, 2020, 17(11): 54–66. doi: 10.23919/JCC.2020.11.005 [2] WANG Haichao, WANG Jinlong, CHEN Jin, et al. Network-connected UAV communications: Potentials and challenges[J]. China Communications, 2018, 15(12): 111–121. doi: 10.12676/j.cc.2018.12.009 [3] GU Jiangchun, DING Guoru, XU Yitao, et al. Proactive optimization of transmission power and 3D trajectory in UAV-assisted relay systems with mobile ground users[J]. Chinese Journal of Aeronautics, 2021, 34(3): 129–144. doi: 10.1016/j.cja.2020.09.028 [4] 单琳锋, 金家才, 张珂. 电子对抗制胜机理[M]. 北京: 国防工业出版社, 2019. [5] MANOJ B S, CHAKRABORTY A, and SINGH R. Complex Networks: A Networking and Signal Processing Perspective[M]. Boston: Prentice Hall, 2018. [6] BAINGANA B and GIANNAKIS G B. Switched dynamic structural equation models for tracking social network topologies[C]. Proceedings of 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Orlando, USA, 2015: 682–686. [7] SILVA T C and ZHAO Liang. Machine Learning in Complex Networks[M]. New York: Springer, 2016. [8] BOCCALETTI S, LATORA V, MORENO Y, et al. Complex networks: Structure and dynamics[J]. Physics Reports, 2006, 424(4/5): 175–308. doi: 10.1016/j.physrep.2005.10.009 [9] MATEOS G, SEGARRA S, MARQUES A G, et al. Connecting the dots: Identifying network structure via graph signal processing[J]. IEEE Signal Processing Magazine, 2019, 36(3): 16–43. doi: 10.1109/MSP.2018.2890143 [10] ZHANG Zhang, ZHAO Yi, LIU Jing, et al. A general deep learning framework for network reconstruction and dynamics learning[J]. Applied Network Science, 2019, 4(1): 110. doi: 10.1007/s41109-019-0194-4 [11] CIMINI G, MASTRANDREA R, and SQUARTINI T. Reconstructing Networks[M]. New York: Cambridge University Press, 2021. [12] KULLMANN L, KERTÉSZ J, and KASKI K. Time-dependent cross-correlations between different stock returns: A directed network of influence[J]. Physical Review E, 2002, 66(2): 026125. doi: 10.1103/PhysRevE.66.026125 [13] ZHANG Zhaoyang, CHEN Yang, MI Yuanyuan, et al. Reconstruction of dynamic networks with time-delayed interactions in the presence of fast-varying noises[J]. Physical Review E, 2019, 99(4): 042311. doi: 10.1103/PhysRevE.99.042311 [14] STETTER O, BATTAGLIA D, SORIANO J, et al. Model-free reconstruction of excitatory neuronal connectivity from calcium imaging signals[J]. PLoS Computational Biology, 2012, 8(8): e1002653. doi: 10.1371/journal.pcbi.1002653 [15] RUNGE J, BATHIANY S, BOLLT E, et al. Inferring causation from time series in Earth system sciences[J]. Nature Communications, 2019, 10(1): 2553. doi: 10.1038/s41467-019-10105-3 [16] WANG Wenxu, LAI Yingcheng, and GREBOGI C. Data based identification and prediction of nonlinear and complex dynamical systems[J]. Physics Reports, 2016, 644: 1–76. doi: 10.1016/j.physrep.2016.06.004 [17] NITZAN M, CASADIEGO J, and TIMME M. Revealing physical interaction networks from statistics of collective dynamics[J]. Science Advances, 2017, 3(2): e1600396. doi: 10.1126/sciadv.1600396 [18] BATTAGLIA P W, HAMRICK J B, BAPST V, et al. Relational inductive biases, deep learning, and graph networks[J/OL]. http://arxiv.org/abs/1806.01261, 2018. [19] ACHARYA H B and GOUDA M G. Brief announcement: The theory of network tracing[C]. Proceedings of the 28th ACM Symposium on Principles of Distributed Computing, Calgary, Canada, 2009: 318–319. [20] YIN Jiabin, LI Youmou, WANG Qi, et al. SNMP-based network topology discovery algorithm and implementation[C]. Proceedings of the 9th International Conference on Fuzzy Systems and Knowledge Discovery, Chongqing, China, 2012: 2241–2244. [21] GAO Yi, DONG Wei, CHEN Chun, et al. Accurate per-packet delay tomography in wireless Ad Hoc networks[J]. IEEE/ACM Transactions on Networking, 2017, 25(1): 480–491. doi: 10.1109/TNET.2016.2594188 [22] NI Jian, XIE Haiyong, TATIKONDA S, et al. Efficient and dynamic routing topology inference from end-to-end measurements[J]. IEEE/ACM Transactions on Networking, 2010, 18(1): 123–135. doi: 10.1109/TNET.2009.2022538 [23] ERIKSSON B, DASARATHY G, BARFORD P, et al. Efficient network tomography for Internet topology discovery[J]. IEEE/ACM Transactions on Networking, 2012, 20(3): 931–943. doi: 10.1109/TNET.2011.2175747 [24] COATES M, RABBAT M, and NOWAK R. Merging logical topologies using end-to-end measurements[C]. Proceedings of the 3rd ACM SIGCOMM Conference on Internet Measurement, Miami Beach, USA, 2003: 192–203. [25] LIANG Yao and LIU Rui. Routing topology inference for wireless sensor networks[J]. ACM SIGCOMM Computer Communication Review, 2013, 43(2): 21–28. doi: 10.1145/2479957.2479961 [26] PARTRIDGE C, COUSINS D, JACKSON A W, et al. Using signal processing to analyze wireless data traffic[C]. Proceedings of the 1st ACM Workshop on Wireless Security, Atlanta, USA, 2002: 67–76. [27] TILGHMAN P and ROSENBLUTH D. Inferring wireless communications links and network topology from externals using Granger causality[C]. Proceedings of the IEEE Military Communications Conference, San Diego, USA, 2013: 1284–1289. [28] MOORE M G and DAVENPORT M A. A Hawkes’ eye view of network information flow[C]. Proceedings of 2016 IEEE Statistical Signal Processing Workshop, Palma de Mallorca, Spain, 2016: 1–5. [29] MOORE M G and DAVENPORT M A. Analysis of wireless networks using Hawkes processes[C]. Proceedings of the 17th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Edinburgh, UK, 2016: 1–5. [30] LAGHATE M and CABRIC D. Learning wireless networks’ topologies using asymmetric Granger causality[J]. IEEE Journal of Selected Topics in Signal Processing, 2018, 12(1): 233–247. doi: 10.1109/JSTSP.2017.2787478 [31] SHARMA P, BUCCI D J, BRAHMA S K, et al. Communication network topology inference via transfer entropy[J]. IEEE Transactions on Network Science and Engineering, 2020, 7(1): 562–575. doi: 10.1109/TNSE.2018.2889454 [32] TESTI E, FAVARELLI E, PUCCI L, et al. Machine learning for wireless network topology inference[C]. Proceedings of the 13th International Conference on Signal Processing and Communication Systems, Gold Coast, Australia, 2019: 1–7. [33] TESTI E and GIORGETTI A. Blind wireless network topology inference[J]. IEEE Transactions on Communications, 2021, 69(2): 1109–1120. doi: 10.1109/TCOMM.2020.3036058 [34] 杨红娃, 潘高峰, 王巍. 战场干线网拓扑推断技术[J]. 通信对抗, 2009(3): 14–17,26.YANG Hongwa, PAN Gaofeng, and WANG Wei. Topology extrapolation method of battlefield backbone networks[J]. Communication Countermeasures, 2009(3): 14–17,26. [35] NIU Zhao, LI Qiang, MA Tao, et al. Research on non-cooperative topology inference method based on node location information[C]. Proceedings of the 18th International Conference on Communication Technology, Chongqing, China, 2018: 271–275. [36] NIU Zhao, MA Tao, SHU Nina, et al. Interference sources localization and communication relationship inference with cognitive radio IoT networks[J]. IEEE Access, 2020, 8: 103062–103072. doi: 10.1109/ACCESS.2020.2998730 [37] 李盛祥, 刘广怡, 陈迎春, 等. 基于帧间隔的CSMA/CA无线网络拓扑推断技术[J]. 信息工程大学学报, 2019, 20(2): 161–167,179. doi: 10.3969/j.issn.1671-0673.2019.02.006LI Shengxiang, LIU Guangyi, CHEN Yingchun, et al. Inter frame space based topology inference for CSMA/CA wireless network[J]. Journal of Information Engineering University, 2019, 20(2): 161–167,179. doi: 10.3969/j.issn.1671-0673.2019.02.006 [38] 唐建强, 李昊, 杨南. 一种基于CSMA/CA协议的无线自组织网络拓扑推断方法[J]. 电子信息对抗技术, 2020, 35(3): 46–49. doi: 10.3969/j.issn.1674-2230.2020.03.009TANG Jianqiang, LI Hao, and YANG Nan. A topology extrapolation method of wireless Ad Hoc network based on CSMA/CA protocol[J]. Electronic Information Warfare Technology, 2020, 35(3): 46–49. doi: 10.3969/j.issn.1674-2230.2020.03.009 [39] 梁爽. 基于复杂网络理论的非合作信息网络分析[D]. [硕士论文], 电子科技大学, 2019.LIANG Shuang. Analysis of non-cooperative information network based on complex network theory[D]. [Master dissertation], University of Electronic Science and Technology of China, 2019. [40] LIU Changkun, WU Xinrong, YAO Changhua, et al. Discovery and research of communication relation based on communication rules of ultrashort wave radio station[C]. Proceedings of the 4th International Conference on Big Data Analytics (ICBDA), Suzhou, China, 2019: 112–117. [41] LIU Changkun, WU Xinrong, ZHU Lei, et al. The communication relationship discovery based on the spectrum monitoring data by improved DBSCAN[J]. IEEE Access, 2019, 7: 121793–121804. doi: 10.1109/ACCESS.2019.2938296 [42] LIU Changkun, WU Xinrong, ZHU Lei, et al. Research on communication network structure mining based on spectrum monitoring data[J]. IEEE Access, 2019, 8: 3945–3959. doi: 10.1109/ACCESS.2019.2952059 [43] 邵豪, 王伦文. 基于压缩感知的无线通信网拓扑推断方法[J]. 探测与控制学报, 2020, 42(2): 92–98.SHAO Hao and WANG Lunwen. Topology inference method for wireless communication networks based on compressed sensing[J]. Journal of Detection &Control, 2020, 42(2): 92–98. [44] 祝晟玮. 基于截获信号分析的通信网多节点信息获取研究[D]. [硕士论文], 电子科技大学, 2020.ZHU Shengwei. Research of multi-node information acquisition in communication network based on interception signal analysis[D]. [Master dissertation], University of Electronic Science and Technology of China, 2020. [45] LIU Zitong, SUN Jiachen, SHEN Feng, et al. Topology sensing of wireless networks based on Hawkes process[J]. Mobile Networks and Applications, 2020, 25(6): 2459–2470. doi: 10.1007/s11036-020-01588-2 [46] LIU Zitong, DING Guoru, WANG Zheng, et al. Cooperative topology sensing of wireless networks with distributed sensors[J]. IEEE Transactions on Cognitive Communications and Networking, 2021, 7(2): 524–540. doi: 10.1109/TCCN.2020.3019601 [47] SONG Yehui, SUN Jiachen, and DING Guoru. Fast topology inference of wireless networks based on Hawkes process[C]. Proceedings of the 13th EAI International Conference on Mobile Multimedia Communications, Harbin, China, 2020: 398–408. [48] SONG Yehui, DING Guoru, SUN Jiachen, et al. Topology tracking of dynamic UAV wireless networks[J/OL]. Chinese Journal of Aeronautics, 2021. [49] 刘子彤, 丁国如, 王威, 等. 面向非合作无线网络的拓扑感知技术分析[J]. 指挥与控制学报, 2021, 7(2): 153–159. doi: 10.3969/j.issn.2096-0204.2021.02.0153LIU Zitong, DING Guoru, WANG Wei, et al. Analysis of topology sensing technology for non-collaborative wireless networks[J]. Journal of Command and Control, 2021, 7(2): 153–159. doi: 10.3969/j.issn.2096-0204.2021.02.0153 [50] PENG Linning, ZHANG Junqing, LIU Ming, et al. Deep learning based RF fingerprint identification using differential constellation trace figure[J]. IEEE Transactions on Vehicular Technology, 2020, 69(1): 1091–1095. doi: 10.1109/TVT.2019.2950670 [51] HALDER S and GHOSAL A. A survey on mobility-assisted localization techniques in wireless sensor networks[J]. Journal of Network and Computer Applications, 2016, 60: 82–94. doi: 10.1016/j.jnca.2015.11.019 [52] NURMINEN H, DASHTI M, and PICHÉ R. A survey on wireless transmitter localization using signal strength measurements[J]. Wireless Communications and Mobile Computing, 2017, 2017: 2569645. doi: 10.1155/2017/2569645 [53] 贾可新. 通信侦察中的信号分选算法研究[D]. [博士论文], 电子科技大学, 2011.JIA Kexin. Research on signal sorting algorithm for communication reconnaissance[D]. [Ph. D. dissertation], University of Electronic Science and Technology of China, 2011. [54] MEI Tiemin and MERTINS A. Convolutive blind source separation based on disjointness maximization of subband signals[J]. IEEE Signal Processing Letters, 2008, 15: 725–728. doi: 10.1109/LSP.2008.2001114 [55] SCHMIDT R. Multiple emitter location and signal parameter estimation[J]. IEEE Transactions on Antennas and Propagation, 1986, 34(3): 276–280. doi: 10.1109/TAP.1986.1143830 [56] HAN J W, KAMBER M A, and PEI J. Data Mining: Concepts and Techniques[M]. 2nd ed. New York: Morgan Kaufmann, 2006. [57] IEEE. IEEE 802.11-2020 IEEE standard for information technology–Telecommunications and information exchange between Systems - Local and metropolitan area networks–Specific requirements - Part 11: Wireless LAN medium access control (MAC) and physical layer (PHY) specifications[S]. IEEE, 2021: 1–7524. [58] SCHREIBER T. Measuring information transfer[J]. Physical Review Letters, 2000, 85(2): 461–464. doi: 10.1103/PhysRevLett.85.461 [59] SHANNON C E. A mathematical theory of communication[J]. The Bell System Technical Journal, 1948, 27(3): 379–423. doi: 10.1002/j.1538-7305.1948.tb01338.x [60] WIENER N. The theory of prediction[M]. BECKENBACH F F. Modern Mathematics for Engineer. New York: McGraw-Hill, 1956. [61] HAWKES A G. Spectra of some self-exciting and mutually exciting point processes[J]. Biometrika, 1971, 58(1): 83–90. doi: 10.1093/biomet/58.1.83 -
下载:
图(12) / 表(1)
计量
- 文章访问数: 764
- HTML全文浏览量: 686
- PDF下载量: 187
- 被引次数: 0
