Performance Investigation of RIS Aided Localization with TDoA in the Near-Field
Bibliographic record
Abstract
Reconfigurable intelligent surfaces (RIS) are forecasted to assume a pivotal role in future wireless communication systems, largely attributed to their capacity to dynamically alter the propagation environment.This study primarily concentrates on the utilization of large RIS, leading to near-field propagation channels, especially in high-frequency communication systems.This is aimed at resolving the complex issue of single anchor-based localization, particularly under conditions where the line-ofsight (LoS) path is prone to severe blockage and fading.In this context, a millimeterwave localization problem, based on time difference of arrival (TDoA), utilizing orthogonal frequency division multiplexing (OFDM) downlink signaling, has been modeled and simulated.The time of arrivals (ToA) measurements, enriched from each RIS tile, are applied to determine user positions.It is recognized that some ToA readings may possess low signal to noise ratio (SNR), making them unsuitable for inclusion in the estimation.Subsequently, various scenarios for RIS tile selection were examined in this research, with the aim of enhancing localization accuracy.Numerical results substantiate the efficacy of the TDoA-RIS algorithm in improving localization accuracy.This is achieved through different strategies to select the most reliable 50% of ToA measurements for the formulation of the estimation procedure.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".