A formulation-aid transfer learning-based framework in received power prediction
Author:
Affiliation:
1. Resilient ICT Research Center, Network Research Institute, National Institute of Information and Communications Technology (NICT)
Publisher
Institute of Electronics, Information and Communications Engineers (IEICE)
Subject
General Medicine
Link
https://www.jstage.jst.go.jp/article/comex/12/2/12_2022XBL0143/_pdf
Reference6 articles.
1. [1] T. Nishio, H. Okamoto, K. Nakashima, Y. Koda, K. Yamamoto, M. Morikura, Y. Asai, and R. Miyatake, “Proactive received power prediction using machine learning and depth images for mmWave networks,” IEEE J. Sel. Areas Commun., vol. 37, no. 11, pp. 2413-2427, Aug. 2019. DOI: 10.1109/JSAC.2019.2933763
2. [2] T. Mikuma, T. Nishio, M. Morikura, K. Yamamoto, Y. Asai, and R. Miyatake, “Transfer learning-based received power prediction using RGB-D camera in mmWave networks,” Proc. IEEE 89th Vehicular Technology Conference (VTC2019-Spring), Kuala Lumpur, Malaysia, May 2019. DOI: 10.1109/VTCSpring.2019.8746643
3. [3] A. Klautau, N. Gonzalez-Prelcic, and R.W. Heath, “LIDAR data for deep learning-based mmWave beam-selection,” IEEE Wireless Commun. Lett., vol. 8, no. 3, pp. 909-912, June 2019. DOI: 10.1109/LWC.2019.2899571
4. [4] K.N. Nguyen and K. Takizawa, “Millimeter-wave received power prediction from time-series images using deep learning,” Proc. IEEE International Conference on Communications (ICC), pp. 5335-5340, Seoul, Korea, Aug. 2022. DOI: 10.1109/ICC45855.2022.9838924
5. [5] K.N. Nguyen and H. Shirai, “Kirchhoff approximation analysis of plane wave scattering by conducting thick slits,” IEICE Trans. Electron., vol. E102-C, no. 1, pp. 12-20, Jan. 2019. DOI: 10.1587/transele.e102.c.12
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