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.
