Realtime Scheduling and Power Allocation Using Deep Neural Networks

Shenghe Xu, Pei Liu, Ran Wang, Shivendra S. Panwar

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

Abstract

With the increasing number of base stations (BSs) and network densification in 5G, interference management using link scheduling and power control are vital for better utilization of radio resources. However, the complexity of solving link scheduling and the power control problem grows exponentially with the number of BS. Due to high computation time, previous methods are useful for research purposes but impractical for real time usage. In this paper we propose to use deep neural networks (DNNs) to approximate optimal link scheduling and power control for the case with multiple small cells. A deep Q-network (DQN) estimates a suitable schedule, then a DNN allocates power for the corresponding schedule. Simulation results show that compared with Geometric Programming based power allocation and exhaustive search based scheduling, the proposed method achieves over five orders of magnitude speed-up with less than nine percent performance loss, making real time usage practical.

Original languageEnglish (US)
Title of host publication2019 IEEE Wireless Communications and Networking Conference, WCNC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538676462
DOIs
StatePublished - Apr 2019
Event2019 IEEE Wireless Communications and Networking Conference, WCNC 2019 - Marrakesh, Morocco
Duration: Apr 15 2019Apr 19 2019

Publication series

NameIEEE Wireless Communications and Networking Conference, WCNC
Volume2019-April
ISSN (Print)1525-3511

Conference

Conference2019 IEEE Wireless Communications and Networking Conference, WCNC 2019
Country/TerritoryMorocco
CityMarrakesh
Period4/15/194/19/19

Keywords

  • deep neural networks
  • deep reinforcement learning
  • power allocation
  • scheduling

ASJC Scopus subject areas

  • General Engineering

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