Robust probabilistic-constrained optimization for IRS-aided MISO communication systems
Le, Tuan Anh 
ORCID: https://orcid.org/0000-0003-0612-3717, Trinh, Van Chien and Di Renzo, Marco
(2021)
Robust probabilistic-constrained optimization for IRS-aided MISO communication systems.
IEEE Wireless Communications Letters, 10
(1)
.
pp. 1-5.
ISSN 2162-2337
[Article]
(doi:10.1109/LWC.2020.3016592)
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Abstract
Taking into account imperfect channel state information, this letter formulates and solves a joint active/passive beamforming optimization problem in multiple-input single-output systems with the support of an intelligent reflecting surface. In particular, we introduce an optimization problem to minimize the total transmit power subject to maintaining the users' signal-to-interference-plus-noise-ratio coverage probability above a predefined target. Due to the presence of probabilistic constraints, the proposed optimization problem is non-convex. To circumvent this issue, we first recast the proposed problem in a convex form by adopting the Bernstein-type inequality, and we then introduce a converging alternating optimization approach to iteratively find the active/passive beamforming vectors. In particular, the transformed robust optimization problem can be effectively solved by using standard interior-point methods. Numerical results demonstrate the effectiveness of jointly optimizing the active/passive beamforming vectors.
| Item Type: | Article |
|---|---|
| Research Areas: | A. > School of Science and Technology |
| Item ID: | 30808 |
| Notes on copyright: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Useful Links: | |
| Depositing User: | Tuan Le |
| Date Deposited: | 14 Aug 2020 12:10 |
| Last Modified: | 29 Nov 2022 18:03 |
| URI: | https://eprints.mdx.ac.uk/id/eprint/30808 |
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