Introductory Articles

• D. Romero and S.-J. Kim, "Radio map estimation: a data-driven approach to spectrum cartography," Vol. 39, 2022, IEEE Signal Processing Magazine.
• M. Höyhtyä, A. Mämmelä, M. Eskola, M. Matinmikko, J. Kalliovaara, J. Ojaniemi, J. Suutala, R. Ekman, R. Bacchus, and D. Roberson, "Spectrum occupancy measurements: a survey and use of interference maps," IEEE Commun. Surveys Tutorials, Vol. 18, 2016.
• M. Pesko, T. Javornik, A. Kosir, M. Stular, and M. Mohorcic, "Radio environment maps: the survey of construction methods," KSII Trans. Internet Information Systems, Vol. 8, 2014.

Theoretical Articles

• D. Romero, T. N. Ha, R. Shrestha, and M. Franceschetti, "Theoretical analysis of the radio map estimation problem," IEEE Trans. Wireless Commun. (Early Access), May 2024.

Empirical Papers

• R. Shrestha, T. N. Ha, P. Q. Viet, and D. Romero, "Radio map estimation: empirical validation and analysis," arXiv preprint arXiv:2310.11036, 2024.
• Z. El-friakh, A. M. Voicu, S. Shabani, L. Simić, and P. Mähönen, "Crowdsourced indoor Wi-Fi REMs: does the spatial interpolation method matter," 2018 IEEE Int. Symp. Dynamic Spectrum Access Netw. 2018.

Radio Map Estimators

The following is a non-exhaustive list of some of the most representative radio map estimation algorithms.

Transformers

Transformers are the technology behind contemporary chatbots such as ChatGPT. They have been recently applied to radio map estimation, leading to state-of-the-art performance with a low computational complexity.

• P. Q. Viet and D. Romero, "Spatial Transformers for Radio Map Estimation," arXiv preprint arXiv:2411.01211, 2024.

Convolutional Neural Networks

Convolutional neural networks are the workhorse of deep learning when it comes to image processing and time series analysis. They have been extensively applied to radio map estimation. They offer strong performance, but their complexity is high.

• R. Shrestha, D. Romero, and S. P. Chepuri, "Spectrum surveying: active radio map estimation with autonomous UAVs," IEEE Trans. Wireless Commun., Vol. 22, 2022. [Code] [Video]
• E. Krijestorac, S. Hanna, and D. Cabric, "Spatial signal strength prediction using 3D maps and deep learning," Proc. IEEE Int Conf. Commun. 2021.
• R. Levie, Ç. Yapar, G. Kutyniok, and G. Caire, "RadioUNet: fast radio map estimation with convolutional neural networks," IEEE Trans. Wireless Commun., Vol. 20, 2021.
• Y. Teganya and D. Romero, "Deep completion autoencoders for radio map estimation," IEEE Trans. Wireless Commun., Vol. 21, 2021. [Code]
• X. Han, L. Xue, F. Shao, and Y. Xu, "A power spectrum maps estimation algorithm based on generative adversarial networks for underlay cognitive radio networks," Sensors, Vol. 20, Jan. 2020.

Kriging

Kriging, originally developed by the geostatistics community, is a quite simple estimator and achieves a very strong performance. Its main limitation is that its complexity grows cubically with the number of measurements. This can be alleviated by online approaches.

• A. Alaya-Feki, S. B. Jemaa, B. Sayrac, P. Houze, and E. Moulines, "Informed spectrum usage in cognitive radio networks: interference cartography," Proc. IEEE Int. Symp. Personal, Indoor Mobile Radio Commun. (Cannes, France), Sep. 2008.
• R. Shrestha, D. Romero, and S. P. Chepuri, "Spectrum surveying: active radio map estimation with autonomous UAVs," IEEE Trans. Wireless Commun., Vol. 22, 2022. [Code] [Video]
• A. Agarwal and R. Gangopadhyay, "Predictive spectrum occupancy probability-based spatio-temporal dynamic channel allocation map for future cognitive wireless networks," Trans. Emerging Telecommun. Technol., Vol. 29, 2018.

Dictionary Learning

• S.-J. Kim and G. B. Giannakis, "Cognitive radio spectrum prediction using dictionary learning," Proc. IEEE Global Commun. Conf. (Atlanta, GA), Dec. 2013.

Kernel Methods

• D. Romero, S-J. Kim, G. B. Giannakis, and R. López-Valcarce, "Learning power spectrum maps from quantized power measurements," IEEE Trans. Signal Process., Vol. 65, May 2017.

Sparsity

• J.-A. Bazerque and G. B. Giannakis, "Distributed spectrum sensing for cognitive radio networks by exploiting sparsity," IEEE Trans. Signal Process., Vol. 58, Mar. 2010.
• J.-A. Bazerque, G. Mateos, and G. B. Giannakis, "Group-lasso on splines for spectrum cartography," IEEE Trans. Signal Process., Vol. 59, Oct. 2011.
• B. A. Jayawickrama, E. Dutkiewicz, I. Oppermann, G. Fang, and J. Ding, "Improved performance of spectrum cartography based on compressive sensing in cognitive radio networks," Proc. IEEE Int. Commun. Conf. (Budapest, Hungary), Jun. 2013.

Matrix Completion

• D. Schäufele, R. L. G. Cavalcante, and S. Mtanczak, "Tensor completion for radio map reconstruction using low rank and smoothness," IEEE Int. Workshop Signal Process. Advances Wireless Commun. (Cannes, France), Jul. 2019.
• B. Khalfi, B. Hamdaoui, and M. Guizani, "AirMAP: Scalable spectrum occupancy recovery using local low-rank matrix approximation," IEEE Glob. Commun. Conf. (Abu Dhabi, UAE), Dec. 2018.

Graphical Models

• T. N. Ha, D. Romero, and R. López-Valcarce, "Radio maps for beam alignment in mmWave communications with location uncertainty," IEEE Vehicular Tech. Conf. Workshop, Spring (Singapore), 2024.