GPF: A GPU-based Design To Achieve ~100 Us Scheduling For 5G NR

Authors:
Yan Huang Virginia Tech
Shaoran Li Virginia Tech
Y. Thomas Hou Virginia Tech
And Wenjing Lou Virginia Tech

Introduction:

a set of diferent OFDM numerologies has been defined in the standards body in 5G New Radio(NR). In this paper, the authors present the design of GPF - a GPU-based proportional fair (PF) scheduler that can meet the ∼100 µ s time requirement.

Abstract:

5G New Radio (NR) is designed to operate under a broad range of frequency bands and support new applications with ultra-low latency. To support its diverse operating conditions, a set of diferent OFDM numerologies has been defined in the standards body. Under this numerology, it is necessary to perform scheduling with a time resolution of ∼100 µ s. This requirement poses a new challenge that does not exist in LTE and cannot be supported by any existing LTE schedulers. In this paper, we present the design of GPF - a GPU-based proportional fair (PF) scheduler that can meet the ∼100 µ s time requirement. The key ideas include decomposing the scheduling problem into a large number of small and independent sub-problems and selecting a subset of sub-problems from the most promising search space to fit into a GPU. By implementing GPF on an of-the-shelf Nvidia Quadro P6000 GPU, we show that GPF is able to achieve near-optimal performance while meeting the ∼100 µ s time requirement. GPF represents the first successful design of a GPU-based PF scheduler that can meet the new time requirement in NR.

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