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GPF: A GPU-based Design to Achieve ~100 μs Scheduling for 5G NR

Authors:

Wenjing LouAuthors Info & Claims

MobiCom '18: Proceedings of the 24th Annual International Conference on Mobile Computing and Networking

Pages 207 - 222

https://doi.org/10.1145/3241539.3241552

Published: 15 October 2018 Publication History

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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 different OFDM numerologies has been defined in the standards body. Under this numerology, it is necessary to perform scheduling with a time resolution of ∼100 μs. 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 μs 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 off-the-shelf Nvidia Quadro P6000 GPU, we show that GPF is able to achieve near-optimal performance while meeting the ∼100 $\mathrmμs time requirement. GPF represents the first successful design of a GPU-based PF scheduler that can meet the new time requirement in NR.

References

[1]

Ericsson Technology Review, "5G new radio: Designing for the future." Available: https://www.ericsson.com/en/ericsson-technology-review/ archive/2017/designing-for-the-future-the-5g-nr-physical-layer

[2]

Qualcomm, "Making 5G NR a commercial reality." Available: https://www.qualcomm.com/media/documents/files/ making-5g-nr-a-commercial-reality.pdf

[3]

Z. E. Ankarali, B. Peköz, and H. Arslan, "Flexible radio access beyond 5G: A future projection on waveform, numerology, and frame design principles,?" IEEE Access, vol. 5, pp. 18295--18309, May 2017.

[4]

3GPP TR 38.913 version 14.3.0, "Study on scenarios and requirements for next generation access technologies." Available: https://portal.3gpp.org/desktopmodules/Specifications/ SpecificationDetails.aspx'specificationId=2996

[5]

3GPP TR 38.804 version 14.0.0, "Study on New Radio access technology; Radio interface protocol aspects." Available: https: //portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails. aspx'specificationId=3070

[6]

3GPP TS 38.211 version 15.0.0, "NR; Physical channels and modulation." Available: https://portal.3gpp.org/desktopmodules/Specifications/ SpecificationDetails.aspx'specificationId=3213

[7]

3GPP TS 38.214 version 15.0.0, "NR; Physical layer procedures for data." Available: https://portal.3gpp.org/desktopmodules/Specifications/ SpecificationDetails.aspx'specificationId=3216

[8]

3GPP TS 38.101--1 version 15.0.0, "NR; User Equipment (UE) radio transmission and reception; Part 1: Range 1 Standalone." Available: https://portal.3gpp.org/desktopmodules/Specifications/ SpecificationDetails.aspx'specificationId=3201

[9]

3GPP TS 38.101--2 version 15.0.0, "NR; User Equipment (UE) radio transmission and reception; Part 2: Range 2 Standalone." Available: https://portal.3gpp.org/desktopmodules/Specifications/ SpecificationDetails.aspx'specificationId=3284

[10]

3GPP TS 38.300 version 15.0.0, "NR; NR and NG-RAN overall description." Available: https://portal.3gpp.org/desktopmodules/Specifications/ SpecificationDetails.aspx'specificationId=3191

[11]

3GPP TR 38.901 version 15.0.0, "Study on channel model for frequencies from 0.5 to 100 GHz." Available: https://portal. 3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx? specificationId=3173

[12]

3GPP TR 22.891 version 14.2.0, "Feasibility study on new services and markets technology enablers; Stage 1." Available: https: //portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails. aspx'specificationId=2897

[13]

3GPP TS 36.211 version 15.0.0, "Evolved Universal Terrestrial Radio Access (E-UTRA); Physical channels and modulation." Available: https://portal.3gpp.org/desktopmodules/Specifications/ SpecificationDetails.aspx'specificationId=2425

[14]

3GPP TS 36.213 version 15.0.0, "Evolved Universal Terrestrial Radio Access (E-UTRA); Physical layer procedures." Available: https://portal.3gpp.org/desktopmodules/Specifications/ SpecificationDetails.aspx'specificationId=2427

[15]

3GPP TS 36.101 version 15.1.0, "Evolved Universal Terrestrial Radio Access (E-UTRA); User Equipment (UE) radio transmission and reception." Available: https://portal.3gpp.org/desktopmodules/Specifications/ SpecificationDetails.aspx'specificationId=2411

[16]

D. Schmalstieg and T. Höllerer, Augmented Reality: Principles and Practice. Reading, MA, USA: Addison-Wesley, 2016.

Digital Library

[17]

G. Burdea, and P. Coiffet, Virtual Reality Technology, 2nd ed. New York, NY, USA: Wiley, 2003.

Digital Library

[18]

L. Kong, M. K. Khan, F. Wu, G. Chen, and P. Zeng, "Millimeter-wave wireless communications for IoT-cloud supported autonomous vehicles: Overview, design, and challenges," IEEE Commun. Mag., vol. 55, no. 1, pp. 62--68, Jan. 2017.

Digital Library

[19]

T. S. Rappaport, Wireless Communications: Principles and Practice. Upper Saddle River, NJ, USA: Prentice-Hall, 1996.

Digital Library

[20]

S. Sesia, I. Toufik, and M. Baker, LTE-The UMTS Long Term Evolution: From Theory to Practice. New York, NY, USA: Wiley, 2009.

[21]

F. Capozzi, G. Piro, L. Grieco, G. Boggia, and P. Camarda, "Downlink packet scheduling in LTE cellular networks: Key design issues and a survey," IEEE Commun. Surveys Tutorials, vol. 15, no. 2, pp. 678--700, 2013.

[22]

O. Grondalen, A. Zanella, K. Mahmood, M. Carpin, J. Rasool, and O. Osterbo, "Scheduling policies in time and frequency domains for LTE downlink channel: a performance comparison," IEEE Trans. Veh. Technol., vol. 66, no. 4, pp. 3345--3360, Apr. 2017.

[23]

A. Stolyar, "On the asymptotic optimality of the gradient scheduling algorithm for multi-user throughput allocation," Operations Research, vol. 53, pp. 12--25, 2005.

Digital Library

[24]

D. Tse, "Multiuser diversity in wireless networks: smart scheduling, dumb antennas and epidemic communication," in IMA Workshop on Wireless Networks, 2001. Available: https://web.stanford.edu/~dntse/papers/ ima810.pdf

[25]

R. Kwan, C. Leung, and J. Zhang, "Proportional fair multiuser scheduling in LTE," IEEE Signal Process. Lett., vol. 16, pp. 461--464, June 2009.

[26]

S. B. Lee, S. Choudhury, A. Khoshnevis, S. Xu, and S. Lu, "Downlink MIMO with frequency-domain packet scheduling for 3GPP LTE," in Proc. IEEE INFOCOM, pp. 1269--1277, Apr. 2009, Rio de Janeiro, Brazil.

[27]

H. Zhang, N. Prasad, and S. Rangarajan, "MIMO downlink scheduling in LTE systems," in Proc. IEEE INFOCOM, pp. 2936--2940, Mar. 2012, Orlando, FL, USA.

[28]

H. S. Liao, P. Y. Chen, and W. T. Chen, "An efficient downlink radio resource allocation with carrier aggregation in LTE-Advanced networks," IEEE Trans. Mobile Computing, vol. 13, no. 10, pp. 2229--2239, Oct. 2014.

[29]

S. Han, K. Jang, K. Park, and S. Moon, "PacketShader: a GPUaccelerated software router," in Proc. ACM SIGCOMM, pp. 195--206, Aug. 2010, New Delhi, India.

Digital Library

[30]

F. Fusco, M. Vlachos, X. Dimitropoulos, and L. Deri, "Indexing million of packets per second using GPUs," in Proc. ACM SIGCOMM-IMC, pp. 327- 332, Oct. 2013, Barcelona, Spain.

Digital Library

[31]

M. Varvello, R. Laufer, F. Zhang, and T. V. Lakshman, "Multilayer packet classification with graphics processing units," IEEE Trans. Networking, vol. 24, no. 5, pp. 2728--2741, Oct. 2016.

Digital Library

[32]

S. Roger, C. Ramiro, A. Gonzalez, V. Almenar, and A. M. Vidal, "Fully parallel GPU implementation of a fixed-complexity soft-output MIMO detector," IEEE Trans. Veh. Technol., vol. 61, no. 8, pp. 3796--3800, Oct. 2012.

[33]

Y. Zhao and F. Lau, "Implementation of decoders for LDPC block codes and LDPC convolutional codes based on GPUs," IEEE Trans. Parallel Distrib. Syst., vol. 25, no. 3, pp. 663--672, Mar. 2014.

Digital Library

[34]

A. Li, R. G. Maunder, B. M. Al-Hashimi, and L. Hanzo, "Implementation of a fully-parallel turbo decoder on a general-purpose graphics processing unit," IEEE Access, vol. 4, pp. 5624--5639, Jun. 2016.

[35]

H.D. Sherali andW. P.Adams, A Reformulation-Linearization Technique for Solving Discrete and Continuous Nonconvex Problems, Chapter 8. Norwell, MA, USA: Kluwer Academic Publishers, 1999.

[36]

Y. T. Hou, Y. Shi, and H. D. Sherali, Applied Optimization Methods for Wireless Networks, Chapter 6. Cambridge, U.K.: Cambridge Univ. Press, 2014.

[37]

G. L. Nemhauser and L. A. Wolsey, Integer and Combinatorial Optimization. New York, NY, USA: John Wiley & Sons, 1999.

[38]

D. P. Bertsekas, Dynamic Programming and Optimal Control. Belmont, MA, USA: Athena Scientific, 1995.

Digital Library

[39]

E. G. Talbi, Metaheuristics: From Design to Implementation. Hoboken, NJ, USA: Wiley, 2009.

[40]

M. R. Zargham, Computer Architecture: Single and Parallel Systems. Upper Saddle River, NJ, USA: Prentice-Hall, 1996.

Digital Library

[41]

IBM ILOG CPLEX Optimizer, software available at http://www01.ibm. com/software/integration/optimization/cplex-optimizer

[42]

Nvidia, "Data sheet: Quadro P6000." Available: https://images.nvidia.com/content/pdf/quadro/data-sheets/ 192152-NV-DS-Quadro-P6000-US-12Sept-NV-FNL-WEB.pdf

[43]

Nvidia, "CUDA C programming guide v9.1." Available: http://docs. nvidia.com/cuda/cuda-c-programming-guide/index.html

[44]

Nvidia, "Optimizing parallel reduction in CUDA." Available: https://developer.download.nvidia.com/compute/cuda/1.1-Beta/x86_ website/projects/reduction/doc/reduction.pdf

[45]

W. Yang, G. Durisi, and E. Riegler, "On the capacity of large-MIMO block-fading channels," IEEE J. Sel. Areas Commun., vol. 31, no. 2, pp. 117- 132, Feb. 2013.

[46]

Nvidia, "Nvidia Tesla P100 -- The most advanced data center accelerator ever built." Available: https://images.nvidia.com/content/pdf/tesla/ whitepaper/pascal-architecture-whitepaper.pdf

[47]

F. Zhang, J. Zhai, B. He, S. Zhang, and W. Chen, "Understanding corunning behaviors on integrated CPU/GPU architectures," IEEE Trans. Parallel Distrib. Syst., vol. 28, no. 3, pp. 905--918, Mar. 2017.

Digital Library

[48]

M. Daga, M. Nutter, and M. Meswani, "Efficient breadth-first search on a heterogeneous processor," in Proc. IEEE Int. Conf. Big Data, pp. 373--382, Oct. 2014, Washington DC, USA.

[49]

Intel, "The compute architecture of Intel Processor Graphics Gen7.5." Available: https://software.intel.com/sites/default/files/managed/4f/e0/ Compute_Architecture_of_Intel_Processor_Graphics_Gen7dot5_Aug4_ 2014.pdf

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Index Terms

GPF: A GPU-based Design to Achieve ~100 μs Scheduling for 5G NR
1. Networks
  1. Network types
    1. Mobile networks

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cover image ACM Conferences

MobiCom '18: Proceedings of the 24th Annual International Conference on Mobile Computing and Networking

October 2018

884 pages

ISBN:9781450359030

DOI:10.1145/3241539

General Chairs:
Rajeev Shorey
TCS Innovation Labs, India
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Rohan Murty
Soroco, USA/India
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Program Chairs:
Yingying (Jennifer) Chen
WINLAB, Rutgers University, USA
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Kyle Jamieson
Princeton University, USA

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Published: 15 October 2018

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MobiCom '18: The 24th Annual International Conference on Mobile Computing and Networking

October 29 - November 2, 2018

New Delhi, India

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Overall Acceptance Rate 440 of 2,972 submissions, 15%

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https://dl.acm.org/doi/10.1145/3636534.3649381
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