Deep Reinforcement Learning Applications For Coexistence in Television Whitespace: A Mini-Review-Reference-Cited by-同舟云学术

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Deep Reinforcement Learning Applications For Coexistence in Television Whitespace: A Mini-Review

Published:2024-04-02 Issue: Volume:11 Page:1-9
ISSN:
Container-title:2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG)
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Author:

Ukpong Udeme¹,Idowu-Bismark Olabode¹,Adetiba Emmanuel¹,Dare Oluwatobi¹,Owolabi Emmanuel²,Kala Raymond Jules³

Affiliation:

1. Covenant University,Department of Electrical and Information Engineering & Covenant Applied Informatics & Communication African Center of Excellene(CApIC-ACE),Ota,Nigeria

2. University of Pretoria,Pretoria,South Africa

3. International University of Grand-Bassam,Grand-Bassam,Côte d'Ivoire

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10629651/10629654/10629684.pdf?arnumber=10629684

Reference21 articles.

1. Quantifying the Availability of TV White Spaces for Cognitive Radio Operation in the UK

2. How Much White-Space Capacity Is There?

3. Second Report and Order and Memorandum Opinion and Order, in the Matter of Unlicensed Operation in the TV Broadcast Bands Additional Spectrum for Unlicensed Devices Below 900mhz and in the 3 GHz Band (et docket 08-260),2008

4. A Survey of Cognitive Radio Access to TV White Spaces

5. Developing a standard for TV white space coexistence: technical challenges and solution approaches

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