Throughput Prediction in Dense IEEE 802.11 WLANs Using Graph Neural Networks
Author:
Publisher
Engineering and Technology Publishing
Subject
Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Information Systems,Software
Link
http://www.jait.us/uploadfile/2023/JAIT-V14N6-1198.pdf
Reference28 articles.
1. [1] B. Bellalta, "IEEE 802.11ax: High-efficiency WLANS," IEEE Wireless Communications, vol. 23, no. 1, pp. 38-46, February 2016. doi: 10.1109/MWC.2016.7422404
2. [2] F. Wilhelmi, S. Barrachina-Munoz, B. Bellalta, C. Cano, A. Jonsson, and V. Ram, "A flexible machine-learning-aware architecture for future WLANs," IEEE Communications Magazine, vol. 58, no. 3, pp. 25-31, March 2020.
3. doi: 10.1109/MCOM.001.1900637
4. [3] S. Szott et al., "Wi-Fi meets ML: A survey on improving IEEE 802.11 performance with machine learning," IEEE Communications Surveys Tutorials, vol. 24, no. 3, pp. 1843-1893, 2022. doi: 10.1109/COMST.2022.3179242
5. [4] M.-S. Kim, T. Ropitault, S. Lee and N. Golmie, "A throughput study for channel bonding in IEEE 802.11ac networks," IEEE Communications Letters, vol. 21, no. 12, pp. 2682-2685, Dec. 2017. doi: 10.1109/LCOMM.2017.2747511
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