Autonomous Throughput Improvement Scheme Using Machine Learning Algorithms for Heterogeneous Wireless Networks Aggregation-Reference-Cited by-同舟云学术

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Autonomous Throughput Improvement Scheme Using Machine Learning Algorithms for Heterogeneous Wireless Networks Aggregation

Published:2012 Issue:4 Volume:E95.B Page:1143-1151
ISSN:0916-8516
Container-title:IEICE Transactions on Communications
language:en
Short-container-title:IEICE Trans. Commun.

Author:

KON Yohsuke¹,HASHIGUCHI Kazuki¹,ITO Masato¹,HASEGAWA Mikio¹²,ISHIZU Kentaro²,MURAKAMI Homare²,HARADA Hiroshi²

Affiliation:

1. Department of Electrical Engineering, Faculty of Engineering, Tokyo University of Science

2. Smart Wireless Laboratory, National Institute of Information and Communications Technology

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Software

Link

https://www.jstage.jst.go.jp/article/transcom/E95.B/4/E95.B_4_1143/_pdf

Reference35 articles.

1. [1] B. Fette, ed., Cognitive Radio Technology, Newnes, Burlington, 2006.

2. [2] J. Mitora, III, “Cognitive radio for flexible mobile multimedia communications,” MoMuC'99, pp.3-10, Nov. 1999.

3. [3] H. Harada, “Software defined radio prototype toward cognitive radio communication systems,” Proc. IEEE DySPAN, vol.1, pp.539-547, Nov. 2005.

4. [4] H. Harada, H. Murakami, K. Ishizu, S. Filin, G. Miyamoto, H.N. Tran, Y.D. Alemseged, and C. Sun, “Research and development on heterogeneous type and spectrum sharing type cognitive radio system,” Proc. IEEE CROWNCOM 2009.

5. [5] ITU-R, “Definitions of software defined radio (SDR) and cognitive radio system (CRS),” Report ITU-R SM.2152, Sept. 2009.

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1. Online machine learning algorithms to optimize performances of complex wireless communication systems;Mathematical Biosciences and Engineering;2021

2. Wireless network optimization method based on cognitive cycle using machine learning;IEICE Communications Express;2018

3. Optimization for Centralized and Decentralized Cognitive Radio Networks;Proceedings of the IEEE;2014-04

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