RT Conference Proceedings
T1 Demo: vrAIn proof-of-concept: a deep learning approach for virtualized RAN resource control
A1 Ayala Romero, José A.
A1 Garcia Saavedra, Andres
A1 Gramaglia, Marco
A1 Costa-Pérez, Xavier
A1 Banchs Roca, Albert
A1 Alcaraz, Juan J.
AB While the application of the NFV paradigm into the network is proceeding full steam ahead, there is still one last milestone to be achieved in this context: the virtualization of the radio access network (vRAN). Due to the very complex dependency between the radio conditions and the computing resources needed to provide the baseband processing functionality, attaining an efficient resource control is particularly challenging. In this demonstration, we will showcase vrAIn, a vRAN dynamic resource controller that employs deep reinforcement learning to perform resource assignment decisions. vrAIn, which is implemented using an open-source LTE stack over a Linux platform, can achieve substantial savings in the used CPU resources while maintaining the target QoS for the attached terminals and maximizing throughput when there is a deficit of computational capacity.
PB Association for Computing Machinery
SN 978-1-4503-6169-9
YR 2019
FD 2019-08-05
LK https://hdl.handle.net/10016/29104
UL https://hdl.handle.net/10016/29104
LA eng
NO Proceeding of: 25th Annual International Conference on Mobile Computing and Networking (MobiCom'19), October 21-25, 2019, Los Cabos, Mexico.
NO The work of University Carlos III of Madrid was supported by H2020 5G-MoNArch project (grant agreement no. 761445) and H2020 5G-TOURS project (grant agreement no. 856950). The work of NEC Laboratories Europe was supported by H2020 5G-TRANSFORMER project (grant agreement no. 761536) and 5GROWTH project (grant agreement no. 856709). The work of University of Cartagena was supported by GrantAEI/FEDER TEC2016-76465-C2-1-R (AIM) and Grant FPU14/03701.
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RD 7 jul. 2024