Super-Resolution Angle Estimation for RIS-Aided Wideband mmWave Communications
Bibliographic record
Abstract
In this paper, we investigate super-resolution angle estimation (SRAE) for reconfigurable intelligent surface (RIS)-aided mmWave communications. For the RIS-aided narrowband system, based on beam sweeping using a wide-beam codebook, we propose a two-step SRAE (TS-SRAE) scheme. In the first step, the selected optimal wide beam is refined to a narrow beam. In the second step, we develop an angle quantization error correction method. Then, for the RIS-aided wideband system, we propose an adaptive codebook design scheme, where the angle domain is divided into two regions, including the central region and the edge region, regarding the beam squint effect. Based on the beam sweeping using the adaptive codebook, we propose a two-region SRAE (TR-SRAE) scheme. In the central region, we extend the TS-SRAE scheme for angle estimation. In the edge region, we formulate the angle estimation as a maximum-a-posteriori problem, which is then solved by our developed Bayesian inference method. Simulation results demonstrate that both TS-SRAE and TR-SRAE schemes can effectively reduce the training overhead and improve the achievable rate.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".