Deployment Optimization of Extremely Large-Scale RIS-Aided Communication System
Deploying an extremely large-scale reconfigurable intelligent surface (XL-RIS) can significantly improve the performance of a RIS-assisted communication system. However, the array aperture and deployment of the XL-RIS affects the radiated field region in which the base station (BS) and the user are located, which in turn affects the performance improvement. In this paper, we have jointly optimized a deployment scheme and phase-shift matrix in XL-RIS-aided communication system, aiming to maximize the user’s received signal-to-noise ratio (SNR). Firstly, we incorporate the far-field and near-field channel into a unified far- or near-field (FoN) model to simplify the SNR analysis and optimization on RIS deployments. Secondly, based on the FoN approach, we derive an expression for the user’s received SNR and formulate an optimization problem to jointly optimize the RIS deployment and phase-shift matrix in order to maximize the user’s received SNR. Thirdly, we summarize the relationship between the RIS array aperture and deployment and the radiated field region in which the BS and the user are located, and propose an optimized closed-form solution for the RIS deployment and phase-shift matrix. Finally, we validate the effectiveness of the proposed scheme through simulation results.
Authors
- Jinting Wang (ORCID: https://orcid.org/0000-0003-4946-2719)
- Shi Jin (ORCID: https://orcid.org/0000-0003-0271-6021)
- Chau Yuen (ORCID: https://orcid.org/0000-0002-9307-2120)
- Xiao Li (ORCID: https://orcid.org/0000-0001-9660-9053)
- Jun Zhang (ORCID: https://orcid.org/0000-0002-8232-2171)
- Yu Han (ORCID: https://orcid.org/0000-0002-3791-6050)
Institutions
- Nanyang Technological University (SG)
- Nanjing University of Posts and Telecommunications (CN)
- Southeast University (CN)
Publication Details
- Journal
- IEEE Transactions on Communications
- Published
- 2025-09-05
- DOI
- https://doi.org/10.1109/tcomm.2025.3606639
- Citations
- 2
- Primary Topic
- Modular Robots and Swarm Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 1.43
Funders
- National Natural Science Foundation of China