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Joint Buffer-Aided Hybrid-Duplex Relay Selection and Power Allocation for Secure Cognitive Networks with Double Deep Q-Network

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journal contribution
posted on 23.04.2021, 13:58 by C Huang, G Chen, Y Gong, Z Han
This paper applies the reinforcement learning in the joint relay selection and power allocation in the secure cognitive radio (CR) relay network, where the data buffers and full-duplex jamming are applied at the relay nodes. Two cases are considered: maximizing the throughput with the delay and secrecy constraints, and maximizing the secrecy rate with the delay constraint, respectively. In both cases, the optimization relies on the buffer states, the interference to/from the primary user, and the constraints on the delay and/or secrecy. This makes it mathematically intractable to apply the traditional optimization methods. In this paper, the double deep Q-network (DDQN) is used to solve the above two optimization problems. We also apply the a-priori information in the CR network to improve the DDQN learning convergence. Simulation results show that the proposed scheme outperforms the traditional algorithm significantly.

Funding

10.13039/501100000266-Engineering and Physical Sciences Research Council; NSFEARS; CNS;

History

Author affiliation

School of Engineering

Version

AM (Accepted Manuscript)

Published in

IEEE Transactions on Cognitive Communications and Networking

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

eissn

2332-7731

Copyright date

2021

Available date

23/04/2021

Language

en