The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks
Open accessAbstract
Reconfigurable intelligent surfaces (RIS) is a promising solution to build a programmable wireless environment via steering the incident signal in fully customizable ways with reconfigurable passive elements. In this paper, we consider a RIS-aided multiuser multiple-input single-output (MISO) downlink communication system. Our objective is to maximize the weighted sum-rate (WSR) of all users by joint designing the beamforming at the access point (AP) and the phase vector of the RIS elements, while both the perfect channel state information (CSI) setup and the imperfect CSI setup are investigated. For perfect CSI setup, a low-complexity algorithm is proposed to obtain the stationary solution for the joint design problem by utilizing the fractional programming technique. Then, we resort to the stochastic successive convex approximation technique and extend the proposed algorithm to the scenario wherein the CSI is imperfect. The validity of the proposed methods is confirmed by numerical results. In particular, the proposed algorithm performs quite well when the channel uncertainty is smaller than 10%.
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
Cites
Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
Shi, Q., Razaviyayn, M., Luo, Z.-Q., & He, C. (2011) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2011.2147784 · Cited by 2,129
Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning
Sun, Y., Babu, P., & Palomar, D. P. (2017) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2016.2601299 · Cited by 1,820
Beyond Massive MIMO: The Potential of Data Transmission With Large Intelligent Surfaces
Hu, S., Rusek, F., & Edfors, O. (2018) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2018.2816577 · Cited by 892
Jung, M., Saad, W., Jang, Y., Kong, G., & Choi, S. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2961990 · Cited by 242
Cited by
Active RIS vs. Passive RIS: Which Will Prevail in 6G?
Zhang, Z., Dai, L., Chen, X., Liu, C., Yang, F., Schober, R., & Poor, H. V. (2023) · IEEE Transactions on Communications · doi:10.1109/tcomm.2022.3231893 · Cited by 1,150
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
Self-citationActive Reconfigurable Intelligent Surface-Aided Wireless Communications
Long, R., Liang, Y.-C., Pei, Y., & Larsson, E. G. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2021.3064024 · Cited by 718
Self-citationSymbiotic Radio: Cognitive Backscattering Communications for Future Wireless Networks
Liang, Y.-C., Zhang, Q., Larsson, E. G., & Li, G. Y. (2020) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2020.3023139 · Cited by 319
Self-citation(2021) · doi:10.1109/twc.2021.3100148 · Cited by 290
Metadata
- Authors
- Guo, H., Liang, Y.-C., Chen, J., & Larsson, E. G.
- Venue
- IEEE Transactions on Wireless Communications
- Published
- 2020-05
- Volume
- 19
- Issue
- 5
- Pages
- 3064-3076
- ISSN
- 1536-1276 (ISSN-L)
- 1558-2248
- OpenAlex
- W3005476696
- Cited by
- 1,013
- References
- 5
- FWCI
- 48.10
- 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: Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks. https://app.cochat.ai/papers/10.1109/twc.2020.2970061
- The paper
- Guo, H., Liang, Y.-C., Chen, J., & Larsson, E. G. (2020). Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks. IEEE Transactions on Wireless Communications. https://doi.org/10.1109/twc.2020.2970061
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.