The DOI matches its Crossref record.
Intelligent Reflecting Surface Assisted Secrecy Communication: Is Artificial Noise Helpful or Not?
Open accessAbstract
In this letter, we investigate whether the use of artificial noise (AN) is helpful to enhance the secrecy rate of an intelligent reflecting surface (IRS) assisted wireless communication system. Specifically, an IRS is deployed nearby a single-antenna receiver to assist in the transmission from a multi-antenna transmitter, in the presence of multiple single-antenna eavesdroppers. Aiming to maximize the achievable secrecy rate, a design problem for jointly optimizing transmit beamforming with AN or jamming and IRS reflect beamforming is formulated, which is however difficult to solve due to its non-convexity and coupled variables. We thus propose an efficient algorithm based on alternating optimization to solve the problem sub-optimally. Simulation results show that incorporating AN in transmit beamforming is beneficial under the new setup with IRS reflect beamforming. In particular, it is unveiled that the IRS-aided design without AN even performs worse than the AN-aided design without IRS as the number of eavesdroppers near the IRS increases.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Toward Smart Wireless Communications via Intelligent Reflecting Surfaces: A Contemporary Survey
Gong, S., Lu, X., Hoang, D. T., Niyato, D., Shu, L., Kim, D. I., & Liang, Y.-C. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3004197 · Cited by 1,035
(2021) · doi:10.1109/twc.2021.3100148 · Cited by 290
An, J., Xu, C., Wang, L., Liu, Y., Gan, L., & Hanzo, L. (2022) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2022.3143226 · Cited by 72
Metadata
- Authors
- Guan, X., Wu, Q., & Zhang, R.
- Venue
- IEEE Wireless Communications Letters
- Published
- 2020-06
- Volume
- 9
- Issue
- 6
- Pages
- 778-782
- ISSN
- 2162-2337 (ISSN-L)
- 2162-2345
- OpenAlex
- W3004201425
- Cited by
- 503
- References
- 0
- FWCI
- 28.61
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Intelligent Reflecting Surface Assisted Secrecy Communication: Is Artificial Noise Helpful or Not? https://app.cochat.ai/papers/10.1109/lwc.2020.2969629
- The paper
- Guan, X., Wu, Q., & Zhang, R. (2020). Intelligent Reflecting Surface Assisted Secrecy Communication: Is Artificial Noise Helpful or Not? IEEE Wireless Communications Letters. https://doi.org/10.1109/lwc.2020.2969629
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.