Joint User, Channel, Modulation-Coding Selection, and RIS Configuration for Jamming Resistance in Multiuser OFDMA Systems
Open accessAbstract
Reconfigurable intelligent surfaces (RISs) can potentially combat jamming. It is non-trivial to perform holistic selections of users, data streams, and modulation-coding modes for all subchannels, and RIS configuration in a downlink multiuser OFDMA system under jamming attacks, because of a mixed-integer program nature and difficulties in acquiring the channel state information (CSI) of the channels to and from the RIS and from an uncooperative jammer. We propose a new deep reinforcement learning (DRL)-based approach that learns through changes in the data rates of the users to reject jamming and maximize the sum rate. The key idea is to decouple the continuous RIS configuration from the discrete selections of users, data streams, subchannels, and modulation-coding modes. Another critical aspect is that we show the optimal selections almost surely follow a winner-takes-all strategy. Accordingly, the new DRL framework learns the RIS configuration with a twin-delayed deep deterministic policy gradient and takes the winner-takes-all strategy to evaluate the reward, thereby reducing the action space and accelerating learning. Simulations show the framework converges fast and fulfills the benefit of the RIS. With no need for the CSI of the channels to and from the RIS and from the jammer, the framework offers practical value.
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Wireless links make it easier for attackers to eavesdrop, jam, manipulate data, or intrude networks than wired networks.
“Data transmissions over wireless links are through airwaves, making it easier for attackers to eavesdrop [75]-[78], jam [79], and manipulate data, e.g., replay attack and Sybil attack, or intrude the networks [80], [81].”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [79]
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“Decentralized WFL has been recently developed, e.g., in [103].”
BackgroundSelf-citation
- Authors
- Yuan, X., Hu, S., Ni, W., Liu, R. P., & Wang, X.
- Affiliations
- Data61 (AU)
- Fudan University (CN)
- School of Electrical and Data Engineering, University of Technology Syndey, Ultimo, Sydney, NSW, Australia
- Venue
- IEEE Transactions on Communications
- Published
- 2023-03
- Volume
- 71
- Issue
- 3
- Pages
- 1631-1645
- ISSN
- 0090-6778 (ISSN-L)
- 1558-0857
- OpenAlex
- W4317497257
- Cited by
- 33
- References
- 40
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 11 · 2023: 9
- FWCI
- 2.45
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Communications (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- OPUS - Open Publications of UTS Scholars (University of Technology Sydney) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (62231010, 62071126, 62101135)
- the Innovation Program of Shanghai Municipal Science (20JC1416400, 21XD1400300)
- China Postdoctoral Science Foundation (2020M681168)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Communication Technologies
- Physical Sciences › Engineering › Aerospace Engineering › Advanced Antenna and Metasurface Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Wireless Communication Security Techniques
- Keywords
- reconfigurable intelligent surface, jamming resistance, user selection, subchannel allocation, RIS configuration, deep reinforcement learning, TD3, sum rate maximization, channel state information
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