Deep Learning for SWIPT: Optimization of Transmit-Harvest-Respond in Wireless-Powered Interference Channel

Woongsup Lee, Kisong Lee, Hyun Ho Choi, Victor C.M. Leung

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


In this paper, we consider a wireless-powered two-way communication, called transmit-harvest-respond, with co-channel interference. The two-way communication considered here comprises three steps: i) transmitters send data signals, ii) receivers decode information and harvest energy simultaneously from the received signals using a policy of time switching (TS) or power splitting (PS), and iii) receivers transmit responses back to transmitters using this harvested energy. We aim to find the transmit power and energy harvesting ratios that maximize the sum rate of the forward links while ensuring a minimum rate requirement for each backward link. Due to the non-convexity and NP hardness of the optimization problem considered here, we first derive suboptimal solutions using an iterative algorithm (IA) on the basis of asymptotic strong duality. In view of the high computation time of the IA, we then design an efficient deep neural network (DNN) framework and novel training strategy as a means of combining supervised and unsupervised training. Specifically, DNNs are pre-trained using the suboptimal solutions obtained by the IA in a supervised manner, as a means of initialization; further training is then applied to DNNs using a well-designed loss function in an unsupervised manner to enhance performance. Simulation results reveal that the pre-training technique using IA solutions is beneficial for improving the performance of the DNN. The proposed hybrid scheme thus achieves near-optimal performances with a lower computation time, compared with the use of IA or DNN alone.

Original languageEnglish
Article number9380295
Pages (from-to)5018-5033
Number of pages16
JournalIEEE Transactions on Wireless Communications
Issue number8
Publication statusPublished - 2021 Aug

Bibliographical note

Funding Information:
Manuscript received September 30, 2020; revised January 3, 2021; accepted March 2, 2021. Date of publication March 17, 2021; date of current version August 12, 2021. This work was supported in part by the Dongguk University Research Fund of 2020 and in part by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (MSIT) under Grant 2019R1A2C4070466. The associate editor coordinating the review of this article and approving it for publication was J. Hoydis. (Corresponding authors: Kisong Lee; Hyun-Ho Choi.) Woongsup Lee is with the Department of Information and Communication Engineering, Gyeongsang National University, Tongyoung 53064, South Korea (e-mail:

Publisher Copyright:
© 2002-2012 IEEE.

All Science Journal Classification (ASJC) codes

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


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