Graduate School and Research Center in Digital Sciences

An artificial intelligence enabled F-RAN testbed

Lu, Zhaoming; Hu, Zhiqun; Han, Zijun; Wang, Luhan; Knopp, Raymond; Zhang, Yuheng

IEEE Wireless Communications, Vol.27, N°2, April 2020

F-RAN is regarded as a promising paradigm for mobile networks to alleviate the unprecedented traffic pressures and meet quality of service requirements of various 5G services with great flexibility. To make F-RAN work in a reliable, efficient, and smart way, AI-enabled F-RAN could be innovative in a number of directions: computing task offloading, resource management, dynamic beam selection, cross-layer design, energy saving and harvesting, mobility enhancement, and so on. In this article, an AI-enabled F-RAN testbed has been designed and implemented in a portable way based on OpenAirInterface, where an AI module is integrated into the F-RAN architecture. The AI module encapsulates the underlying operators of various machine learning frameworks to help a network make policies for different applications. Based on the proposed testbed, the F-RAN research community can easily analyze and evaluate their novel methods and quickly develop intelligent algorithms in a lab environment.

Doi Bibtex

Title:An artificial intelligence enabled F-RAN testbed
Type:Journal
Language:English
City:
Date:
Department:Communication systems
Eurecom ref:6257
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Bibtex: @article{EURECOM+6257, doi = {http://dx.doi.org/10.1109/MWC.001.1900386}, year = {2020}, month = {05}, title = {{A}n artificial intelligence enabled {F}-{RAN} testbed}, author = {{L}u, {Z}haoming and {H}u, {Z}hiqun and {H}an, {Z}ijun and {W}ang, {L}uhan and {K}nopp, {R}aymond and {Z}hang, {Y}uheng}, journal = {{IEEE} {W}ireless {C}ommunications, {V}ol.27, {N}°2, {A}pril 2020}, url = {http://www.eurecom.fr/publication/6257} }
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