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Mean-Field Multi-Agent Contextual Bandit for Energy-Efficient Resource Allocation in vRANs

In this paper, we gather data from our experimental platform and compare the performance and energy consumption of a HA (NVIDIA GPU V100) vs. a CPU (Intel Xeon Gold 6240R, 16 cores) for energy-friendly software processing.

Information

Radio Access Network (RAN) virtualization, key for new-generation mobile networks, requires Hardware Accelerators (HAs) that swiftly process wireless signals from Base Stations (BSs) to meet stringent reliability targets. However, HAs are expensive and energy-hungry, which increases costs and has serious environmental implications. To address this problem, we gather data from our experimental platform and compare the performance and energy consumption of a HA (NVIDIA GPU V100) vs. a CPU (Intel Xeon Gold 6240R, 16 cores) for energy-friendly software processing.

https://ieeexplore.ieee.org/document/10621197

Author/Speaker/Contributor

Jose A. Ayala-Romero, Leonardo Lo Schiavo, Andres Garcia-Saavedra, Xavier Costa-Perez

Event/Publication

IEEE

Date

12 August 2024