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A robustness analysis of learning-based coexistence mechanisms for LTE-U operation in non-stationary conditions

Author
Perez-Romero, J.; Sallent, J.; Ferrús, R.; Agusti, R.
Type of activity
Presentation of work at congresses
Name of edition
2015 IEEE 82nd Vehicular Technology Conference Fall
Date of publication
2015
Presentation's date
2015-09
Book of congress proceedings
2015 IEEE 82nd Vehicular Technology Conference (VTC Fall): proceedings: Boston, Massachusetts: 6-9 September 2015
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
DOI
https://doi.org/10.1109/VTCFall.2015.7390815 Open in new window
Repository
http://hdl.handle.net/2117/84973 Open in new window
URL
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=7390815 Open in new window
Abstract
The use of Long Term Evolution (LTE) in the unlicensed 5 GHz band, referred to as LTE-U, is a promising enhancement to increase the capacity of LTE networks and meet the requirements of future systems. This paper considers a Q-learning based Channel Selection strategy to decide the most appropriate channel to use for downlink traffic offloading in the unlicensed band as a mechanism to greatly facilitate the coexistence among several LTE-U and/or Wi-Fi systems in the same band. The focus is place...
Citation
Perez, J., Sallent, J., Ferrús, R., Agusti, R. A robustness analysis of learning-based coexistence mechanisms for LTE-U operation in non-stationary conditions. A: IEEE Vehicular Technology Conference Fall. "2015 IEEE 82nd Vehicular Technology Conference (VTC Fall): proceedings: Boston, Massachusetts: 6-9 September 2015". Boston, Massachusetts: Institute of Electrical and Electronics Engineers (IEEE), 2015.
Keywords
Bandwidth 5 GHz, Channel selection, Downlink traffic offloading, LTE networks capacity, LTE-U operation, Learning based coexistence mechanisms, Long Term Evolution, Nonstationary conditions, Q-learning, Wi-Fi systems
Group of research
CCABA - Advanced Broadband Communications Center
GRCM - Mobile Communication Reserach Group

Participants