Q-Learning-Based Spatial Reuse Method Considering Throughput Fairness by Negative Reward for High Throughput-Reference-Cited by-同舟云学术

Q-Learning-Based Spatial Reuse Method Considering Throughput Fairness by Negative Reward for High Throughput

Published:2022 Issue: Volume: Page:207-219
ISSN:1867-8211
Container-title:Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
language:
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Author:

Takematsu Mirai^ORCID,Sakai Shota^ORCID,Kunibe Masashi^ORCID,Shigeno Hiroshi^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-94822-1_12

Reference11 articles.

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2. Nishide, K., Kubo, H., Shinkuma, R., Takahashi, T.: Detecting hidden and exposed terminal problems in densely deployed wireless networks. IEEE Trans. Wireless Commun. 11(11), 3841–3849 (2012)

3. Jamil, I., Cariou, L., Hélard, J.-F.: Novel learning-based spatial reuse optimization in dense WLAN deployments. EURASIP J. Wirel. Commun. Netw. 2016(1), 1–19 (2016). https://doi.org/10.1186/s13638-016-0632-2

4. Ak, E., Canberk, B.: FSC: two-scale AI-driven fair sensitivity control for 802.11ax networks. In: GLOBECOM 2020–2020 IEEE Global Communications Conference, pp. 1–6 (2020)

5. Wilhelmi, F., Barrachina-Muñoz, S., Bellalta, B., Cano, C., Jonsson, A., Neu, G.: Potential and pitfalls of multi-armed bandits for decentralized spatial reuse in WLANs. J. Netw. Comput. Appl. 127, 26–42 (2019)