Learning Centric Power Allocation for Edge Intelligence

Shuai Wang, Rui Wang, Qi Hao, Yik Chung Wu, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

While machine-type communication (MTC) devices generate massive data, they often cannot process this data due to limited energy and computation power. To this end, edge intelligence has been proposed, which collects distributed data and performs machine learning at the edge. However, this paradigm needs to maximize the learning performance instead of the communication throughput, for which the celebrated water-filling and max-min fairness algorithms become inefficient since they allocate resources merely according to the quality of wireless channels. This paper proposes a learning centric power allocation (LCPA) method, which allocates radio resources based on an empirical classification error model. To get insights into LCPA, an asymptotic optimal solution is derived. The solution shows that the transmit powers are inversely proportional to the channel gain, and scale exponentially with the learning parameters. Experimental results show that the proposed LCPA algorithm significantly outperforms other power allocation algorithms.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Communications, ICC 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728150895
DOIs
StatePublished - Jun 2020
Event2020 IEEE International Conference on Communications, ICC 2020 - Dublin, Ireland
Duration: Jun 7 2020Jun 11 2020

Publication series

NameIEEE International Conference on Communications
Volume2020-June
ISSN (Print)1550-3607

Conference

Conference2020 IEEE International Conference on Communications, ICC 2020
CountryIreland
CityDublin
Period6/7/206/11/20

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Keywords

  • Classification error model
  • edge intelligence
  • learning centric communication
  • multiple-input multiple-output

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  • Cite this

    Wang, S., Wang, R., Hao, Q., Wu, Y. C., & Poor, H. V. (2020). Learning Centric Power Allocation for Edge Intelligence. In 2020 IEEE International Conference on Communications, ICC 2020 - Proceedings [9148872] (IEEE International Conference on Communications; Vol. 2020-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICC40277.2020.9148872