The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Anti-Jamming Communications Using Spectrum Waterfall: A Deep Reinforcement Learning Approach
Open accessAbstract
This letter investigates the problem of anti-jamming communications in a dynamic and intelligent jamming environment through machine learning. Different from existing studies which need to know (estimate) the jamming patterns and parameters, we use the temporal and spectral information, i.e., the spectrum waterfall, directly. First, to cope with the challenge of infinite state of spectrum waterfall, a recursive convolutional neural network is designed. Then, an anti-jamming deep reinforcement learning algorithm is proposed to obtain the optimal anti-jamming strategies. Finally, simulation results validate the proposed approach. The proposed algorithm does not need to model the jamming patterns, and naturally has the ability to explore the unknown environment, which implies that it can be widely used for combating dynamic and intelligent jamming.
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Nguyen, T. T., & Reddi, V. J. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3121870 · Cited by 620
Secure Transmission Scheme Based on Joint Radar and Communication in Mobile Vehicular Networks
Yao, Y., Shu, F., Li, Z., Cheng, X., & Wu, L. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3271452 · Cited by 278
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Uprety, A., & Rawat, D. B. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3040957 · Cited by 214
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Luong, N. C., Hoang, D. T., Gong, S., Niyato, D., Wang, P., Liang, Y.-C., & Kim, D. I. (2018) · arXiv · doi:10.48550/arxiv.1810.07862 · Cited by 17
Metadata
- Authors
- Liu, X., Xu, Y., Jia, L., Wu, Q., & Anpalagan, A.
- Venue
- IEEE Communications Letters
- Published
- 2018-05
- Volume
- 22
- Issue
- 5
- Pages
- 998-1001
- ISSN
- 1089-7798 (ISSN-L)
- OpenAlex
- W2765492968
- Cited by
- 258
- References
- 10
- FWCI
- 16.34
- Type
- journal article
- Language
- en
- Open access
- green
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