Page 93 - ITU Journal Future and evolving technologies Volume 2 (2021), Issue 4 – AI and machine learning solutions in 5G and future networks

ITU Journal on Future and Evolving Technologies, Volume 2 (2021), Issue 4

[2] ITU‑T . ITU AI/ML in 5G Challenge (2019). Available [14] Qi, H., Huang, H., Hu, Z., Wen, X., & Lu, Z.
at: https://www.itu.int/en/ITU‑T/AI/challenge/ (2020). “On‑demand channel bonding in heteroge‑
2020/Pages/default.aspx neous WLANs: A multi‑agent deep reinforcement
learning approach”. Sensors, 20(10), 2789.
[3] Wilhelmi, F ., Barrachina‑Muñoz, S., Bellalta, B., Cano,
C., Jonsson, A., & Neu, G. (2019). “Potential and pit‑ [15] Pan, C., Cheng, Y ., Yang, Z., & Zhang, Y . (2018, July).
falls of multi‑armed bandits for decentralized spatial “Dynamic opportunistic spectrum access with chan‑
reuse in WLANs”. Journal of Network and nel bonding in mesh networks: A game‑theoretic
Computer Applications, 127, 26‑42. approach”. In International Conference on Machine
Learning and Intelligent Communications
[4] Francesc Wilhelmi. (2020). [ITU‑T AI Chal‑ (pp. 381‑390). Springer, Cham.
lenge] Input/Output of project “Improv‑
ing the capacity of IEEE 802.11 WLANs [16] Bianchi, G. (2000). “Performance analysis of the
through Machine Learning” [Data set]. Zenodo. IEEE 802. 11 distributed coordination function”. IEEE
http://doi.org/10.5281/zenodo.4106127 Journal on selected areas in communications, 18(3),
535‑547.
[5] Bellalta, B. (2016). IEEE 802.11 ax: “High‑ef iciency
WLANs”. IEEE Wireless Communications, 23(1), 38-46. [17] Bellalta, B., Zocca, A., Cano, C., Checco, A., Barcelo, J.,
& Vinel, A. (2014). “Throughput analysis in CSMA/CA
[6] Lo ́ pez‑Pérez, D., Garcia‑Rodriguez, A., Galati‑ networks using continuous time Markov networks:
Giordano, L., Kasslin, M., & Doppler, K. (2019). A tutorial”. Wireless Networking for Moving Objects,
“IEEE 802.11 be extremely high throughput: The 115‑133.
next generation of Wi‑Fi technology beyond 802.11 ax”.
IEEE Communications Magazine, 57(9), 113‑119. [18] Baccelli, F ., & Błaszczyszyn, B. (2009). “Stochastic
geometry and wireless networks (Vol. 1)”. Now Pub‑
[7] Barrachina-Muñoz, S., Wilhelmi, F., & Bellalta, B. lishers Inc.
dis-
[19] Feng, H., Shu, Y ., Wang, S., & Ma, M. (2006, June).
tributed high-density WLANs”. IEEE Transactions on
“SVM‑based models for predicting WLAN traf ic”. In
Mobile Computing.
2006 IEEE International Conference on Communica‑
[8] Barrachina-Muñoz, S., Wilhelmi, F ., & Bellalta, B. tions (Vol. 2, pp. 597‑602). IEEE.
(2019). “To overlap or not to overlap: Enabling chan‑
[20] Abbas, M., Elhamshary, M., Rizk, H., Torki, M., &
nel bonding in high‑density WLANs”. Computer Net‑
Youssef, M. (2019, March). “WiDeep: WiFi‑based ac‑
works, 152, 40‑53.
curate and robust indoor localization system using
deep learning”. In 2019 IEEE International Confer‑
[9] Cao, R., & Zhang, H. (2019). U.S. Patent Application No.
16/435,899. ence on Pervasive Computing and Communications
(PerCom (pp. 1‑10). IEEE.
[10] &
[21] Davaslioglu, K., Soltani, S., Erpek, T ., & Sagduyu, Y .
adaptation
(2019). “DeepWiFi: Cognitive WiFi with deep learn‑
Mobile
ing”. IEEE Transactions on Mobile Computing.
Computing, 16(1), 243-256.
[22] Khan, M. A., Hamila, R., Al‑Emadi, N. A., Kiranyaz, S.,
[11] Huang, P., Yang, X., & Xiao, L. (2016). “Dynamic
channel bonding: Enabling lexible spectrum ag‑ & Gabbouj, M. (2020). “Real‑time throughput predic‑
gregation”. IEEE Transactions on Mobile Computing, tion for cognitive Wi‑Fi networks”. Journal of Network
15(12), 3042‑3056. and Computer Applications, 150, 102499.
[23] Barrachina‑Muñoz, S., Wilhelmi, F ., Selinis, I., & Bel‑
[12] Chen, Y. D., Wu, D. R., Sung, T. C., & Shih, K. P.
lalta, B. (2019, April). “Komondor: a wireless
(2018, April). “DBS: A dynamic bandwidth selection
network simulator for next‑generation high‑density
MAC protocol for channel bonding in IEEE 802.11 ac
WLANs”. In 2019 Wireless Days (WD) (pp. 1‑8). IEEE.
WLANs”. In 2018 IEEE Wireless Communications and
Networking Conference (WCNC) (pp. 1-6). IEEE. [24] Wilhelmi, F ., Barrachina‑Muñoz, S., Cano, C., Selinis,
I., & Bellalta, B. (2021). “Spatial reuse in IEEE 802.11
[13] Nabil, A., Abdel-Rahman, M. J., & MacKenzie, A. B. ax WLANs”. Computer Communications.
(2017, October). “Adaptive channel bonding in wire-
less LANs under demand uncertainty”. In 2017 IEEE [25] ITU‑T , ML5G‑I‑237 (2020). “A compilation of prob‑
28th Annual International Symposium on Personal, lem statements and resources for ITU Global Chal‑
Indoor, and Mobile Radio Communications (PIMRC) lenge on AI/ML in 5G networks”. Available on‑
(pp. 1-7). IEEE. line: https://www.itu.int/en/ITU‑T/AI/challenge/
2020/Documents/ML5G‑I‑237‑R5_v10.docx

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