Channel Estimation and Localization for Cylindrical RIS-Assisted Multi-User ISAC Systems
Bibliographic record
Abstract
In this paper, we investigate the channel estimation and localization problems for integrated sensing and communication (ISAC) systems empowered by the reconfigurable intelligent surface (RIS) technology. We propose a cylindrical RIS architecture that arranges reflecting elements on a curved substrate, where the three-dimensional array manifold can not only offer a 360° coverage but also perceive the environmental information more deeply. The conformal RIS topology can fit the deployment scenarios more flexibly, which, however, incurs a potential issue of shadowing effect, i.e., signal waves from/to certain directions can only be observed by a part of reflectors due to the shielding of the substrate curvature, yielding different visibility regions (VRs) for multiple users on the RIS array manifold. In order to address this problem, we propose a tensorial channel estimation approach, where the cascaded channel is transformed into the beamspace domain and modeled as a canonical polyadic tensor. By leveraging the principle of tensor completion, we can eliminate the RIS training profiles to deconstruct the channel in the element domain. Then, we develop a VR detection strategy based on the sliding windows, retrieving equivalent channel parameters from the effective signal responses. Finally, by exploring the characteristics of the cylindrical RIS architecture, we develop a decoupling framework to uniquely recover the exact channel parameters, based on which each user can locate itself and other interacting ones. Simulation results indicate that the proposed cylindrical RIS can enable the channel estimation, user localization and data transmission simultaneously, exhibiting remarkable performance under the shadowing effect interference.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".