Joint relay and channel selection in relay‐aided anti‐jamming system: A reinforcement learning approach
Author:
Affiliation:
1. College of Communications Engineering Army Engineering University of PLA Nanjing China
2. PLA 32381 Troops Beijing China
Funder
National Natural Science Foundation of China
Publisher
Wiley
Subject
Electrical and Electronic Engineering
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1002/ett.4243
Reference29 articles.
1. Decode-and-Forward Buffer-Aided Relay Selection in Cognitive Relay Networks
2. Exploiting Sparsity for Multiple Relay Selection with Relay Gain Control in Large AF Relay Networks
3. An Anti-Jamming Hierarchical Optimization Approach in Relay Communication System via Stackelberg Game
4. Dynamic Spectrum Anti-Jamming Communications: Challenges and Opportunities
5. SlimeniF ScheersB NirVL ChtourouZ AttiaR. Learning multi‐channel power allocation against smart jammer in cognitive radio networks. Paper presented at: Proceedings of the 2016 International Conference on Military Communications and Information Systems (ICMCIS) Bangkok Thailand; May 23‐24 2016:1‐7.
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2. Deep Reinforcement Learning-Based Computation Offloading for Anti-jamming in Fog Computing Networks;Proceedings of the 2022 12th International Conference on Communication and Network Security;2022-12
3. Multipower‐level Q ‐learning algorithm for random access in nonorthogonal multiple access massive machine‐type communications systems;Transactions on Emerging Telecommunications Technologies;2022-04-10
4. Data‐driven approach to design energy‐efficient joint precoders at source and relay using deep learning in MIMO‐CRNs;Transactions on Emerging Telecommunications Technologies;2022-02-09
5. Joint relay and channel selection against mobile and smart jammer: A deep reinforcement learning approach;IET Communications;2021-07-27
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