A Novel Approach of Semi-blind Frequency Selection for HF Regional Emergency Maneuver Communication

  • Dai-hui MoEmail author
  • Guo-jun Li
  • Xiao-fei Xu
  • Lu Tan
  • Ya-kun Xing
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 251)


Shortwave regional mobile communication relies on regional ionospheric vertical detector for frequency forecast. The inherent properties of full band, high power and fixed detection result in the difficulty of real-time deployment in complex terrains. In this case, from the perspective of communication fusion detection, with comprehensive utilization of broadband passive monitoring and low SNR detection technology, we propose a semi-blind frequency selection mechanism for regional mobile shortwave communication. First, we acquire the optimal scanning frequency in the working frequency band based on the full range of passive monitoring, which abandons the electromagnetic pollution made by the full band scanning. This mechanism can act as the basis for the use of existing narrow band shortwave radio bidirectional detection. Then, we get the active optimal frequency perception based on the portable shortwave radio and the optimal frequency selection. Finally, we work out the problem of low efficiency and poor concealment in the high-power independent detection, which is of great significance to the promotion of the regional emergency mobile shortwave communication in complex environments.


Cognitive radio Active detection system Passive frequency selection Real-time spectrum monitoring 



This work was supported in part by the National Natural Science Foundation of China (61671452), Natural Science Foundation of Chongqing(Cstc2015jcyjBX0078, Cstc2016jcyjA0556), Chongqing Society Livelihood Security Project(cstc2016shm-szx40003) and Chongqing Industry Key Technology Innovation Project(cstc2017z-dcyzdyfx0011).


  1. 1.
    Song, Z., Liu, Y., et. al: Cognitive Radio Technology and Application. Defense Industry Press (2012)Google Scholar
  2. 2.
    Tan, X., Lin, G.: Frequency prediction method of shortwave communication based on flight test. Radio Commun. Technol. (03) (2014)Google Scholar
  3. 3.
    Li, X.: Chirp detection technology and its application in shortwave communication. Ship Electron. Eng. (2005)Google Scholar
  4. 4.
    Yang, K., Ye, X.: Overview of shortwave channel quality assessment technology. Telecommun. Technol. 53(8), 1113–1118 (2013)Google Scholar
  5. 5.
    Yang, Z.Z.: Study on spectrum sensing technology of shortwave network. J. Commun. Technol. 47(11), 1318–1321 (2014)Google Scholar
  6. 6.
    Zhang, T., Liu, A., Shao, L.: Cognitive radio technology and its application in shortwave communication frequency selection. Commun. World 12(8), 12–17 (2016)Google Scholar
  7. 7.
    Yan, J.-F., Guo, R., Tian, H.: Study on hollow rate and time-effect of short-wave dynamic spectrum based on cognition. School Sci. Technol. 33(6), 57–60 (2011)Google Scholar
  8. 8.
    Shahid, A., Ahmad, S., Akram, A., et al.: Cognitive ALE for HF radios. In: Proceedings of Second International Conference on Computer Engineering and Applications, Bali Island Indonesia, pp. 28–33. IEEE (2010)Google Scholar
  9. 9.
    Blau, G.: Shortwave Communication Line Engineering Design. Electronic Industry Press, Beijing (1987)Google Scholar
  10. 10.
    Stewart, F.G.: Ionospheric Communications Enhanced Profile Analysis Circuit (ICEPAC) Prediction Program Technical Manual. Institute for Telecommunication Sciences, USA, pp. 46–59 (2008)Google Scholar
  11. 11.
    Hu, Z.: Modern Shortwave Communication. Defense Industry Press (2009)Google Scholar
  12. 12.
    Dai, Y.: Shortwave Digital Communication System Adaptive Frequency Selection Technology. Zhejiang Science and Technology PressGoogle Scholar
  13. 13.
    Li, D.: Frequency management of shortwave communication system. Radio Commun. Technol. (2009)Google Scholar
  14. 14.
    Zhang, W., Mallik, R.K., Letaief, K.B.: Cooperative spectrum sensing optimization in cognitive radio networks. In: 2008 IEEE International Conference on Communications (2008)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Dai-hui Mo
    • 1
    • 2
    Email author
  • Guo-jun Li
    • 3
  • Xiao-fei Xu
    • 3
  • Lu Tan
    • 3
  • Ya-kun Xing
    • 3
  1. 1.Department of Electronic EngineeringTsinghua UniversityBeijingChina
  2. 2.Academy of Military Sciences PLA ChinaBeijingChina
  3. 3.HF Communications Engineering Lab of CQChongqing Communication CollegeChongqingChina

Personalised recommendations