Learning-Based Resource Management for SWIPT

Kisong Lee, Woongsup Lee

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

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
Volume14
Issue number4
DOIs
StatePublished - Dec 2020

Keywords

  • Energy efficiency
  • energy harvesting
  • interference channel
  • neural network (NN)
  • power splitting

Fingerprint

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

Cite this