NDLI: A novel access network selection scheme using Q-learning algorithm for cognitive terminal

Content Provider	IEEE Xplore Digital Library
Author	Haifeng Tan Yizhe Li Yami Chen Li Tan Qian Li
Copyright Year	2010
Description	Author affiliation: The State Radio Monitoring Center; Beijing 100037, China (Haifeng Tan) \|\| Key Laboratory of Universal Wireless Communications, Ministry of Education, Wireless Technology Innovation Institute (WTI), Beijing University of Posts and Telecommunications, China, 100876 (Yizhe Li; Yami Chen; Li Tan; Qian Li)
Abstract	In a B3G/4G wireless communication system, the users will connect to the network using one of several available radio access technologies. In this paper, we proposed a Q-learning based algorithm for terminals' independent access network selection with the aim of improving the resource utilization and providing the best quality of service with respect to the wireless environment status, network performance and user' requirement. In particular, for the first time we introduced the concept of low-carbon as one of the evaluation indicators of wireless communication performance, in order to reduce the power consumption and achieve a balance between quality and consumption. The proposed scheme is based on the concept of cognitive network, which has been proposed recently by the motivation of complexity, heterogeneity and reliability requirements of tomorrow's network and the cognitive pilot channel used in it. The performance of the access network selection algorithm is shown in the simulation and it can be seen that this algorithm significantly reduced the blockrate and power consumption as well as increased the throughput compared with random accessing approach. In future work, we will continue to research on the effective access network selection algorithm and try to introduce the low-carbon indicator to other aspects of the wireless communication system.
Starting Page	1
Ending Page	5
File Size	527714
Page Count	5
File Format	PDF
e-ISBN	9780984589333
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	ICST
Subject Keyword	Wireless LAN cognitive network access network selection Q-learning algorithm low-carbon
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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