Research on three-dimensional spectrum mapping driven by propagation model

Qiuming Zhu, Xiaofu Du, Qihui Wu, Jie Wang, Yi Zhao, Kai Mao, Weizhi Zhong

Article ID: 1668
Vol 5, Issue 2, 2022, Article identifier:1-15

VIEWS - 140 (Abstract) 53 (PDF)


Spectrum map is the foundation of spectrum resource management, security governance and spectrum warfare. Aiming at the problem that the traditional spectrum mapping is limited to two-dimensional space, a three-dimensional spectrum data acquisition and mapping system architecture for the integration of space, sky and earth is presented, and a spectrum map reconstruction scheme driven by propagation model is proposed, which can achieve high-precision three-dimensional spectrum map rendering under the condition of sparse sampling. The spectrum map reconstructed by this method in the case of single radiation source and multiple radiation sources is in good agreement with the theoretical results based on ray tracing method. In addition, the measured results of typical scenes further verify the feasibility of this method.


Spectrum Space; Spectrum Situation; Spectrum Map; Spectrum Mapping; Communication Model; Ray Tracing

Full Text:



Ran Z, Chang J, Rong Z, et al. Research on the construction of radio environment map based on revised spatial interpolation. Application of Electronic Technique 2018; 237: 405–415.

Hu Y, Zhang R. Differentially-private incentive mechanism for crowd sourced radio environment map construction. IEEE Conference on Computer Communications; 2019 Apr 29–May 2; Paris. 2019. p. 1594–1602.

Lu J. Research on spectral map reconstruction algorithm under incomplete observation conditions (in Chinese) [MSc thesis]. Changsha: National Defense University; 2018.

Xia H, Cha S, Huang J, et al. Review and prospect of electromagnetic spectrum map construction methods (in Chinese). Chinese Journal of Radio Science 2020; 35(4): 445–456.

Lu J, Cha S, Huang J, et al. A method to complete the spectrum map based on the difference of observed values (in Chinese). Journal of Microwaves 2018; 34 (S2): 426–430.

Du X, Zhu M, Wu Q, et al. UAV-assisted spectrum mapping system based on tensor completion scheme. MILICOM 2020: Machine Learning and Intelligent Communications. Cham: Springer; 2020. p. 16–26.

Bourdena A, Pallis E, Kormentzas G, et al. Real-time TVWS trading based on a centralized CR network architecture. IEEE International Work-shop on Recent Advances in Cognitive Communications and Networking; 2011 Dec 5–9; Houston. 2011. p. 964–969.

European Commission. Flexible and spectrum-aware radio access through measurements and modelling in cognitive radio systems. 2020.

Guo X, Zhang Y, Chen Z, et al. Distributed electromagnetic spectrum detection system based on self-organizing network. 12th International Symposium on Antennas, Propagation and EM Theory (ISAPE); 2018 Dec 3–6; Hangzhou. 2018. p. 1–5.

Patino M, Vega F. Model for measurement of radio environment maps and location of white spaces for cognitive radio deployment. IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications (APWC); 2018 Sep 10–14; Cartagena 2018. p. 913–915.

Melvasalo M, Koivunen V, Lundn J. Spectrum maps for cognition and co-existence of communication and radar systems. 50th Asilomar Conference on Signals, Systems and Computers; 2016 Nov 6–9; Pacific Grove. 2016. p. 58–63.

Janakaraj P, Wang P, Chen Z. Towards cloud-based crowd-augmented spectrum mapping for dynamic spectrum access. International Conference on Computer Communication and Networks; 2016 Aug 1–4; Waikoloa. 2016. p. 1–7.

Hou F. Research on spectrum prediction technology in cognitive radio networks (in Chinese) [MSc thesis]. Beijing: Beijing University of Posts and Telecommunications; 2017.

Guo Y, Shao W, Zhang Y, et al. A hybrid interpolation and extrapolation method for dynamic electromagnetic environment map construction (in Chinese). Audio Engineering 2020; 44(10): 72–76.

Lazzaro D, Montefusco L. Radial basis functions for the multivariate interpolation of large scattered data sets. Journal of Computational and Applied Mathematics 2002; 40(1): 521–536.

Denkovski D, Atanasovski V, Gavrilovska L, et al. Reliability of a radio environment map: Case of spatial interpolation techniques. 7th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM); 2012 Jun 18–20; Stockholm. 2012. p. 248–253.

Zi R, Chang J, Zong R, et al. Radio environment map generation technology based on improved spatial interpolation (in Chinese). Application of Electronic Technique 2018; 44(3): 103–107.

Zi R. Research on the construction method of radio environment map (in Chinese) [MSc thesis]. Kunming: Yunnan University; 2018.

Cha S, Lu J, Huang J, et al. Nonparametric spectrum map construction method based on monitoring data (in Chinese). Journal of Microwaves 2018; 34(S2): 431–434.

Tang M, Ding G, Wu Q, et al. A joint tensor completion and prediction scheme for multi-dimensional spectrum map construction. IEEE Access 2016; 4: 8044–8052.

Tang M, Ding G, Zhen X, et al. Multi-dimensional spectrum map construction: A tensor perspective. 8th International Conference on Wireless Communications & Signal Processing (WCSP); 2016 Oct 13–15; Yangzhou. 2016. p. 1–5.

Feng Q. Research on cooperative spectrum sensing algorithm of cognitive radio system based on tensor analysis (in Chinese) [MSc thesis]. Jilin: Jilin Institute of Chemical Technology; 2020.

Federal Communications Commission. Spectrum policy task force. Rep ET Docket No.02–135. Washington: FCC; 2002.

Zhe C, Qiu R. Prediction of channel state for cognitive radio using higher-order hidden Markov model. Proceedings of the IEEE SoutheastCon 2010 (SoutheastCon); 2010 Mar 18–21; Concord. 2010. p. 276–282.

Zhao Q, Lang T, Swami A, et al. Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: A POMDP framework. IEEE Journal on Selected Areas in Communications 2007; 25(3): 589–600.

Zhao Q, Lang T, Swami A. Decentralized cognitive mac for dynamic spectrum access. First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks; 2005 Nov 8–11; Baltimore. 2005. p.224–232.

Wen Z, Tao L, Xiang W, et al. Autoregressive spectrum hole prediction model for cognitive radio systems. IEEE International Conference on Communications Workshops; 2008 May 19–23; Beijing. 2008. p. 154–157.

Gao X, Ren G, Chen J, et al. Spectrum prediction based on support vector regression in fast changing channel environment (in Chinese). Journal of Signal Processing 2014; 30(3): 289–297

Jia Y, Qiu L, Wei H. Spectrum occupancy prediction based on K-nearest neighbor regression (in Chinese). Telecommunication Engineering 2016; 56(8): 844–849.

Tumuluru VK, Wang P, Niyato D. A neural network-based spectrum prediction scheme for cognitive radio. IEEE International Conference on Communications; 2010 May 23–27; Cape Town. 2010. p. 1–5.

Yin L, Yin SX, Hong W, et al. Spectrum behavior learning in cognitive radio based on artificial neural network. MILCOM 2011 Military Communications Conference; 2011 Nov 7–10; Baltimore. 2011. p. 25–30.

Bai S, Zhou X, Xu F. Spectrum prediction based on improved-back-propagation neural networks. 11th International Conference on Natural Computation (ICNC); 2015 Aug 15–17; Zhangjiajie. 2015. p. 1006–1011.

Sato K, Inage K, Fujii T. Radio environment map construction with joint space-frequency interpolation. 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC); 2020 Feb 19–21; Fukuoka. 2020. p. 51–54.

Wang M, Sheng Y, Huang Y, et al. Spatial interpolation method of electromagnetic geographical environment monitoring data (in Chinese). Journal of Geo-information Science 2017; 19(7): 872–879.

Yilmaz HB, Tugcu T. Location estimation-based radio environment map construction in fading channels. Wireless Communications & Mobile Computing 2015; 15(3): 561–570.

Zhu MQ, Dang XY, Xu DZ, et al. High efficient rejection method for generating Nakagamim sequences. Electronic Letter 2011; 47(19): 1100–1101.

Gajewski P. Propagation models in radio environment map design. 2018 Baltic URSI Symposium (URSI); 2018 May 15–17; Poznan. 2018. p. 234–237.

Zhu Q, Wang Y, Jiang K, et al. 3D non-stationary geometry-based multi-input multi-output channel model for UAV-ground communication systems. IET Microwaves, Antennas & Propagation 2019; 13(8): 1104–1112.

Yao M, Chen X, Wang J, et al. Ray tracing-based path loss modeling for UAV-to-ground mmWave channels in campus scenario. MILICOM 2020: Machine Learning and Intelligent Communications Engineering. 2020. p. 459–470.

Shepard D. A two-dimensional interpolation function for irregularly-spaced data. The 23rd ACM National Conference; 1968 Jan; New York. 1968. p. 517–524.

Azpurua MA, Ramos KD. A comparison of spatial interpolation methods for estimation of average electromagnetic field magnitude. Progress in Electromagnetics Research M 2010; 14: 135–145.



  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Creative Commons License

This site is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.