Constrained Deep Learning for Wireless Resource Management

Hoon Lee, Sang Hyun Lee, Tony Q.S. Quek

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

11 Citations (Scopus)


In this paper, we investigate a deep learning (DL) approach to solve a generic constrained optimization problem in wireless networks, where the objective and constraint functions can be nonconvex. To this target, the computation process of the solution is replaced by deep neural networks (DNNs). The original problem is transformed to a training task of the DNNs subject to nonconvex constraints. Since existing DL libraries are originally intended for unconstrained training, they cannot be directly applied to our constrained training problem. We propose a constrained training strategy based on the primal-dual method from optimization techniques. The proposed DL approach is deployed to solve transmit power allocation problems in various network configurations. The simulation results shed light on the feasibility of the DL method as an alternative to existing optimization algorithms.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Communications, ICC 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538680889
Publication statusPublished - 2019 May
Externally publishedYes
Event2019 IEEE International Conference on Communications, ICC 2019 - Shanghai, China
Duration: 2019 May 202019 May 24

Publication series

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


Conference2019 IEEE International Conference on Communications, ICC 2019

Bibliographical note

Funding Information:
This work was supported in part by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (2016-0-00208, High Accurate Positioning Enabled MIMO Transmission and Network Technologies for Next 5G-V2X (vehicle-to-everything) Services).

Publisher Copyright:
© 2019 IEEE.

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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