Deep Learning-Aided Distributed Transmit Power Control for Underlay Cognitive Radio Network

Woongsup Lee, Kisong Lee

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


In this paper, we investigate deep learning-aided distributed transmit power control in the context of an underlay cognitive radio network (CRN). In the proposed scheme, the fully distributed transmit power control strategy of secondary users (SUs) is learned by means of a distributed deep neural network (DNN) structure in an unsupervised manner, such that the average spectral efficiency (SE) of the SUs is maximized whilst allowing the interference on primary users (PUs) to be regulated properly. Unlike previous centralized DNN-based strategies that require complete channel state information (CSI) to optimally determine the transmit power of SU transceiver pairs (TPs), in our proposed scheme, each SU TP determines its own transmit power based solely on its local CSI. Our simulation results verify that the proposed scheme can achieve a near-optimal SE comparable with a centralized DNN-based scheme, with a reduced computation time and no signaling overhead.

Original languageEnglish
Article number9384291
Pages (from-to)3990-3994
Number of pages5
JournalIEEE Transactions on Vehicular Technology
Issue number4
Publication statusPublished - 2021 Apr

Bibliographical note

Publisher Copyright:
© 1967-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Computer Networks and Communications
  • Automotive Engineering


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