Near-Field Channel Reconstruction in Sensing RIS-Assisted Wireless Communication Systems
Bibliographic record
Abstract
A reconfigurable intelligent surface (RIS) with active elements is an augmented version of an RIS. By equipping all or part of RIS elements with signal processing capabilities, the channel estimation and the design of RIS phases can be further extended, yielding an improvement in the spectral efficiency (SE). In this paper, we first present a novel sensing RIS structure which is efficient for hardware implementation. Unlike partial active elements in previous structures, all elements are available to the RF chains via switches, which enables the traditional channel estimation methods and channel extrapolation to be implemented. Moreover, we make a comprehensive analysis and comparison with other RIS structures from the perspective of channel state information (CSI) acquisition. Considering the large-scale of RIS and base station (BS) array, we model the channel between the user and the RIS, the RIS and the BS using a near-field channel model. Based on the structured channel model, we propose a low-overhead channel reconstruction protocol through a parameter-extracting method, while the training overhead and complexity are also analyzed. In addition, we investigate the RIS elements’ activation strategy to further reduce the training overhead. Finally, numerical results demonstrate that the proposed scheme achieves accurate channel estimation with low overhead, which can also enhance the achievable SE.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".