Channel Estimation in RIS-Aided mmWave Wireless Systems Using Matching Pursuit With Phase Rotation
Bibliographic record
Abstract
Reconfigurable Intelligent Surface (RIS) is considered one of the most promising technologies for the next generation of wireless communication networks. Despite its great potential, RIS faces new challenges in integrating efficiently into wireless networks, including reflection optimization, channel estimation (CE), and optimization of deployment locations. This paper presents and analyzes a CE solution in RIS-assisted systems using compressive sensing techniques. The steering vector and the complex channel gains of the base station (BS)-RIS/user equipment (UE)-RIS links are estimated separately by using thematching pursuitwith phase rotation (MP-PR) by deploying a few active elements at the RIS panel. The performance and complexity of the proposed method are comprehensively analyzed in different scenarios and compared against those of several other related methods in the literature. Numerical results show the effectiveness of the proposed method, which achieves better (or at least very similar) performance compared to other relevant techniques but with a significant decrease in computational complexity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".