Machine Learning for Wireless Communication Channel Modeling: An Overview

Author:

Aldossari Saud MobarkORCID,Chen Kwang-Cheng

Funder

FC2 Collaborative Seed Grant

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Computer Science Applications

Reference95 articles.

1. Jiang, Z., He, Z., Chen, S., Molisch, A.F., Zhou, S., & Niu, Z. (2018). Inferring remote channel state information: Cramér-Rae lower bound and deep learning implementation. In IEEE Global Communications Conference (GLOBECOM), (pp. 1–7). https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8648140&tag=1 .

2. 3GPP. (2017). TR 38.900 version 14.2.0 release, study on channel model for frequency spectrum above 6 GHz.

3. 3GPP. (2018). TS 29.520, 5G system; Network data analytics services.

4. Kristem, V., Bas, C. U., Wang, R., & Molisch, A. F. (2018). Outdoor wideband channel measurements and modeling in the 318 GHz band. IEEE Transactions on Wireless Communications, 17(7), 4620–4633. https://doi.org/10.1109/TWC.2018.2828001 .

5. Mao, Q., Hu, F., & Hao, Q. (2018). Deep learning for intelligent wireless networks: A comprehensive survey. IEEE Communications Surveys and Tutorials, 20(4), 2595–2621. https://doi.org/10.1109/COMST.2018.2846401 .

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