Channel Estimation and Tracking in STAR-RIS Aided Systems: A Recurrent Quantum Learning Framework
Bibliographic record
Abstract
A modular quantum machine learning (QML) based framework for channel estimation and tracking in STAR-RIS aided wireless systems, is proposed. While STAR-RISs enable extensive 360-degree manipulation of the propagation channels compared to reflection-only RISs, it is not a trivial task to acquire and track channel information in STAR-RIS aided systems, as this needs to be done for the devices located in both the reflection zone and the transmission zone of the surface. Powered by quantum-based operations, which are characterized by quantum parallelism, QML holds the potential to handle high-dimensional channel estimation and tracking in STAR-RIS systems. Towards this end, a novel modular QML framework that employs different quantum-based learning modules is proposed. Its purpose, threefold in design, strives to (i) eliminate the noise inherited in the coarse channel information, (ii) estimate the channels of devices in the reflection and transmission regions, and (iii) update the estimated channels to accommodate the time-varying movements of these devices. The numerical results demonstrate that the proposed QML-based approach outperforms the one based on classical recurrent neural networks.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".