Bibliographic record
Abstract
To meet the rising demand for data rates, the next generation of wireless communication will need to deploy more antennas at the base stations (BSs) while also addressing sustainability and power efficiency concerns. Recently, metasurfaces, specifically dynamic metasurface antennas (DMAs), have been suggested as potential solutions. Specifically, researchers have demonstrated the potential throughput performance of DMAs as basestation antennas. In this paper, we study the channel estimation problem for DMAs, modeled as correlated multiple input multiple output (MIMO) systems. We first propose a system model that details the noise sources in these systems. Then, exploiting the fast response time of DMAs, we consider the possibility of multiple channel measurements during a single symbol. Adopting a minimum-mean-square-error (MMSE) based approach and assuming availability of the long-term channel correlation matrices at the receiver and transmitters, then, we formulate the channel estimation problem. We first solve this problem assuming full-control over the DMA. We then propose a more practical algorithm to map the full-control solution to the DMA structure, thereby accounting for the limitations imposed by the DMA. Our numerical results demonstrate that the proposed practical algorithm suffers only a minor performance loss compared to the full-control case.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".