Resource Allocation for Multi-Channel Underlay Cognitive Radio Network Based on Deep Neural Network

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In this letter, a resource allocation strategy based on a deep neural network (DNN) is proposed for multi-channel cognitive radio networks, where the secondary user (SU) opportunistically utilizes channels without causing excessive interference to the primary user (PU). In the proposed scheme, the allocation of transmit power in each channel for SUs is found by utilizing the newly proposed DNN model, which separately determines the overall transmit power of individual SUs and the proportion of transmit power allocated to each channel. Both the spectral efficiency (SE) of the SU and the amount of interference caused to the PU are considered in the training of the DNN model, such that the interference caused to the PUs can be properly regulated while the SE of the SU is improved. Through simulations, we show that our scheme enables a high SE of the SU to be achieved while the interference caused to the PU can be maintained at less than the threshold.

Original languageEnglish
Article number8419776
Pages (from-to)1942-1945
Number of pages4
JournalIEEE Communications Letters
Issue number9
Publication statusPublished - 2018 Sept

Bibliographical note

Funding Information:
Manuscript received June 27, 2018; accepted July 16, 2018. Date of publication July 25, 2018; date of current version September 8, 2018. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1B07040796) and also supported by Rural Development Administration(RDA) through Cooperative Research Program for Agriculture Science & Technology Development, funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA) (Project No. PJ01229901201801). The associate editor coordinating the review of this paper and approving it for publication was D. Ciuonzo.

Publisher Copyright:
© 2018 IEEE.

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Computer Science Applications
  • Electrical and Electronic Engineering


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