DNN Based Beam Selection in mmW Heterogeneous Networks
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Springer International Publishing
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https://link.springer.com/content/pdf/10.1007/978-3-030-87473-5_16
Reference15 articles.
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2. Alkhateeb, A.: DeepMIMO: a generic deep learning dataset for millimeter wave and massive MIMO applications. arXiv:1902.06435 (2019)
3. Alrabeiah, M., Alkhateeb, A.: Deep learning for mmWave beam and blockage prediction using sub-6GHz channels. arXiv:1910.02900 (2019)
4. Anton-Haro, C., Mestre, X.: Data-driven beam selection for mmWave communications with machine and deep learning: an angle of arrival-based approach. In: 2019 IEEE International Conference on Communications Workshops (ICC Workshops), pp. 1–6, May 2019. https://doi.org/10.1109/ICCW.2019.8756991
5. Dias, M., Klautau, A., González-Prelcic, N., Heath, R.W.: Position and LIDAR-aided mmWave beam selection using deep learning. In: 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pp. 1–5, July 2019. https://doi.org/10.1109/SPAWC.2019.8815569
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