Wireless network optimization method based on cognitive cycle using machine learning-Reference-Cited by-同舟云学术

Wireless network optimization method based on cognitive cycle using machine learning

Published:2018 Issue:7 Volume:7 Page:278-283
ISSN:2187-0136
Container-title:IEICE Communications Express
language:en
Short-container-title:IEICE ComEX

Author:

Oshima Koji¹²,Kobayashi Takumu¹,Taenaka Yuki¹,Kuroda Kaori¹,Hasegawa Mikio¹

Affiliation:

1. Tokyo University of Science

2. Kozo Keikaku Engineering Inc.

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

General Medicine

Link

https://www.jstage.jst.go.jp/article/comex/7/7/7_2018XBL0061/_pdf

Reference11 articles.

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2. [2] S. Haykin, “Cognitive radio: Brain-empowered wireless communications,” IEEE J. Sel. Areas Commun., vol. 23, no. 2, pp. 201–220, 2005. DOI:10.1109/JSAC.2004.839380

3. [3] IEEE Standard for Architectural Building Blocks Enabling Network-Device Distributed Decision Making for Optimized Radio Resource Usage in Heterogeneous Wireless Access Networks, IEEE Std.1900.4, 2009.

4. [4] M. Hasegawa, H. Hirai, K. Nagano, H. Harada, and K. Aihara, “Optimization for centralized and decentralized cognitive radio networks,” Proc. IEEE, vol. 102, no. 4, pp. 574–584, 2014. DOI:10.1109/JPROC.2014.2306255

5. [5] Y. Kon, K. Hashiguchi, M. Ito, M. Hasegawa, K. Ishizu, H. Murakami, and H. Harada, “Autonomous throughput improvement scheme using machine learning algorithms for heterogeneous wireless networks aggregation,” IEICE Trans. Commun., vol. E95.B, no. 4, pp. 1143–1151, 2012. DOI:10.1587/transcom.E95.B.1143

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