Deep neural network based resource allocation for V2X communications

Jin Gao*, Muhammad R. A. Khandaker, Faisal Tariq, Kai-Kit Wong, Risala T. Khan

*Corresponding author for this work

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

38 Citations (Scopus)
61 Downloads (Pure)


This paper focuses on optimal transmit power allocation to maximize the overall system throughput in a vehicle-to-everything (V2X) communication system. We propose two methods for solving the power allocation problem namely the weighted minimum mean square error (WMMSE) algorithm and the deep learning-based method. In the WMMSE algorithm, we solve the problem using block coordinate descent (BCD) method. Then we adopt supervised learning technique for the deep neural network (DNN) based approach considering the power allocation from the WMMSE algorithm as the target output. We exploit an efficient implementation of the mini-batch gradient descent algorithm for training the DNN. Extensive simulation results demonstrate that the DNN algorithm can provide very good approximation of the iterative WMMSE algorithm yet reducing the computational overhead significantly.

Original languageEnglish
Title of host publication2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)
ISBN (Electronic)9781728112206
Publication statusPublished - 7 Nov 2019
Event90th IEEE Vehicular Technology Conference 2019 - Honolulu, United States
Duration: 22 Sept 201925 Sept 2019

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252
ISSN (Electronic)2577-2465


Conference90th IEEE Vehicular Technology Conference 2019
Abbreviated titleVTC 2019 Fall
Country/TerritoryUnited States


  • Deep learning
  • Deep neural network
  • Machine learning
  • Power control
  • Resource allocation
  • V2V
  • V2X

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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