Learning-Based Resource Management for SWIPT

Kisong Lee, Woongsup Lee

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


In this article, we consider the joint optimization of transmit power and power splitting ratio to maximize the energy efficiency in a simultaneous wireless information and power transfer based interference channel, in which receivers use a power splitting policy to harvest energy from a wireless signal. We propose an optimization-based iterative algorithm (O-IA) from well-known optimization techniques as a comparative scheme, and also devise a neural network based learning algorithm (NN-LA) to deal with nonconvexity caused by cochannel interference among multiple nodes. Through simulations, we provide a comparative study of the two approaches in terms of energy efficiency and time complexity. In particular, we find that NN-LA achieves a near-optimal energy efficiency, whereas its time complexity is significantly reduced, in comparison with O-IA.

Original languageEnglish
Article number9031335
Pages (from-to)4750-4753
Number of pages4
JournalIEEE Systems Journal
Issue number4
Publication statusPublished - 2020 Dec

Bibliographical note

Publisher Copyright:
© 2007-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


Dive into the research topics of 'Learning-Based Resource Management for SWIPT'. Together they form a unique fingerprint.

Cite this