An ML-based Spectrum Sharing Technique for Time-Sensitive Applications in Industrial Scenarios
Author:
Affiliation:
1. Birmingham City University,College of Computing,Birmingham,United Kingdom
2. University of Surrey,Guildford,United Kingdom
3. University of Hertfordshire,Hatfield,United Kingdom
Funder
Ministry of Education
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10592105/10592309/10592619.pdf?arnumber=10592619
Reference12 articles.
1. On the Fairness of Wi-Fi and LTE-LAA Coexistence
2. 5 GHz RLAN; Harmonised Standard covering the essential requirements of article 3.2 of Directive 2014/53/EU, Broadband Radio Access Networks (BRAN),2017
3. Machine Learning Enabled Spectrum Sharing in Dense LTE-U/Wi-Fi Coexistence Scenarios
4. A Machine Learning Algorithm for Unlicensed LTE and WiFi Spectrum Sharing
5. Spectrum Sharing between Cellular and Wi-Fi Networks based on Deep Reinforcement Learning
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