Exploiting deep learning for secure transmission in an underlay cognitive radio network

Miao Zhang, Kanapathippillai Cumanan, Jeyarajan Thiyagalingam, Yanqun Tang, Wei Wang, Zhiguo Ding, Octavia A. Dobre

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

This paper investigates a machine learning-based power allocation design for secure transmission in a cognitive radio (CR) network. In particular, a neural network (NN)-based approach is proposed to maximize the secrecy rate of the secondary receiver under the constraints of total transmit power of secondary transmitter, and the interference leakage to the primary receiver, within which three different regularization schemes are developed. The key advantage of the proposed algorithm over conventional approaches is the capability to solve the power allocation problem with both perfect and imperfect channel state information. In a conventional setting, two completely different optimization frameworks have to be designed, namely the robust and non-robust designs. Furthermore, conventional algorithms are often based on iterative techniques, and hence, they require a considerable number of iterations, rendering them less suitable in future wireless networks where there are very stringent delay constraints. To meet the unprecedented requirements of future ultra-reliable low-latency networks, we propose an NN-based approach that can determine the power allocation in a CR network with significantly reduced computational time and complexity. As this trained NN only requires a small number of linear operations to yield the required power allocations, the approach can also be extended to different delay sensitive applications and services in future wireless networks. When evaluate the proposed method versus conventional approaches, using a suitable test set, the proposed approach can achieve more than 94% of the secrecy rate performance with less than 1% computation time and more than 93% satisfaction of interference leakage constraints. These results are obtained with significant reduction in computational time, which we believe that it is suitable for future real-time wireless applications.

Original languageBritish English
Article number9318524
Pages (from-to)726-741
Number of pages16
JournalIEEE Transactions on Vehicular Technology
Volume70
Issue number1
DOIs
StatePublished - Jan 2021

Keywords

  • Cognitive radio networks
  • deep learning
  • neural network
  • physical layer security
  • resource allocation techniques

Fingerprint

Dive into the research topics of 'Exploiting deep learning for secure transmission in an underlay cognitive radio network'. Together they form a unique fingerprint.

Cite this