Deep Learning Based Resource Assignment for Wireless Networks

Minseok Kim, Hoon Lee, Hongju Lee, Inkyu Lee

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


This letter studies a deep learning approach for binary assignment problems in wireless networks, which identifies binary variables for permutation matrices. This poses challenges in designing a structure of a neural network and its training strategies for generating feasible assignment solutions. To this end, this letter develop a new Sinkhorn neural network which learns a non-convex projection task onto a set of permutation matrices. An unsupervised training algorithm is proposed where the Sinkhorn neural network can be applied to network assignment problems. Numerical results demonstrate the effectiveness of the proposed method in various network scenarios.

Original languageEnglish
Pages (from-to)3888-3892
Number of pages5
JournalIEEE Communications Letters
Issue number12
Publication statusPublished - 2021 Dec 1

Bibliographical note

Publisher Copyright:
© 1997-2012 IEEE.


  • Deep learning
  • Sinkhorn operator
  • assignment problem

ASJC Scopus subject areas

  • Modelling and Simulation
  • Computer Science Applications
  • Electrical and Electronic Engineering


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