Correlation-Based Multi-Stage Channel Estimation for RIS-Assisted Multi-User mmWave Systems
Bibliographic record
Abstract
This paper proposes a correlation-based multi-stage channel estimation strategy for reconfigurable intelligent surface (RIS)-assisted multi-user (MU) millimeter wave (mmWave) systems, in which the base station (BS), the RIS and the users are all equipped with a uniform planar array (UPA). Specifically, by exploiting the correlation of angle information during multiple consecutive channel coherence blocks, we estimate the full channel state information in the first coherence block (FCB) and only update the gain information in each subsequent block. Then, based on the ambiguity property, a low-pilot-overhead two-stage method is adopted in the FCB. In Stage 1, all users jointly transmit pilot to estimate the angles of arrival at the BS and the correlation factors between different paths of the common BS-RIS channel via introducing a set of matching matrices and a designed RIS phase shift matrix, which achieves a significant MU diversity gain. By exploiting the mmWave cascaded channels' ambiguity property, an ambiguous common BS-RIS channel is constructed with the estimated correlation factors. In Stage 2, each user independently transmits a limited number of pilots to estimate their individual ambiguous RIS-user channel, leading to a comprehensive cascaded channel estimation. For the remaining blocks, we adopt a straightforward least-squares method for rapid gain estimation. Simulation results confirm the method's effectiveness in providing accurate estimation with reduced pilot overhead, outperforming existing methods.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".