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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.20354 |
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| _version_ | 1866911228185542656 |
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| author | Guo, Huayan Rao, Junhui Wong, Alex M. H. Murch, Ross Lau, Vincent K. N. |
| author_facet | Guo, Huayan Rao, Junhui Wong, Alex M. H. Murch, Ross Lau, Vincent K. N. |
| contents | Pixel-based reconfigurable intelligent surfaces (RISs) employ a novel design to achieve high reflection gain at a lower hardware cost by eliminating the phase shifters used in traditional RIS. However, this design presents challenges for channel estimation and passive beamforming due to its non-separable state response, rendering existing solutions ineffective. To address this, we first approximate the non-separable RIS response functions using a kernel-based method and a deep neural network, achieving high accuracy while reducing computational and memory complexity. Next, we propose a simplified cascaded channel model that focuses on dominated scattering paths with limited unknown parameters, along with customized algorithms to estimate short-term and long-term parameters separately. Finally, we introduce a low-complexity passive beamforming algorithm to configure the discrete RIS state vector, maximizing the achievable rate. Our simulation results demonstrate that the proposed solution significantly outperforms various baselines across a wide SNR range. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_20354 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Channel Estimation and Passive Beamforming for Pixel-based Reconfigurable Intelligent Surfaces with Non-Separable State Response Guo, Huayan Rao, Junhui Wong, Alex M. H. Murch, Ross Lau, Vincent K. N. Signal Processing Pixel-based reconfigurable intelligent surfaces (RISs) employ a novel design to achieve high reflection gain at a lower hardware cost by eliminating the phase shifters used in traditional RIS. However, this design presents challenges for channel estimation and passive beamforming due to its non-separable state response, rendering existing solutions ineffective. To address this, we first approximate the non-separable RIS response functions using a kernel-based method and a deep neural network, achieving high accuracy while reducing computational and memory complexity. Next, we propose a simplified cascaded channel model that focuses on dominated scattering paths with limited unknown parameters, along with customized algorithms to estimate short-term and long-term parameters separately. Finally, we introduce a low-complexity passive beamforming algorithm to configure the discrete RIS state vector, maximizing the achievable rate. Our simulation results demonstrate that the proposed solution significantly outperforms various baselines across a wide SNR range. |
| title | Channel Estimation and Passive Beamforming for Pixel-based Reconfigurable Intelligent Surfaces with Non-Separable State Response |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2510.20354 |