Joint Buffer-Aided Hybrid-Duplex Relay Selection and Power Allocation for Secure Cognitive Networks with Double Deep Q-Network.pdf (718.86 kB)
Joint buffer-aided hybrid-duplex relay selection and power allocation for secure cognitive networks with double deep Q-network
journal contribution
posted on 2021-05-06, 11:28 authored by Chong Huang, Gaojie Chen, Yu GongYu Gong, Zhu HanThis 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
Communications Signal Processing Based Solutions for Massive Machine-to-Machine Networks (M3NETs)
Engineering and Physical Sciences Research Council
Find out more...NSF EARS-1839818, CNS1717454, CNS-1731424, and CNS-1702850
History
School
- Mechanical, Electrical and Manufacturing Engineering
Published in
IEEE Transactions on Cognitive Communications and NetworkingVolume
7Issue
3Pages
834-844Publisher
IEEEVersion
- AM (Accepted Manuscript)
Rights holder
© IEEEPublisher statement
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Publication date
2021-03-03Copyright date
2021eISSN
2332-7731Publisher version
Language
- en
Depositor
Dr Yu Gong. Deposit date: 5 May 2021Usage metrics
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