Positioning and Tracking Using Reconfigurable Intelligent Surfaces and Extended Kalman Filter
Bibliographic record
Abstract
The downlink time-difference-of-arrival (DL-TDOA), which is used for positioning in 3GPP NR, is the time interval that is measured by a user equipment (UE) between the reception of the downlink signals from two different cells. The measurement of the DL-TDOA might be challenging, especially at a cell center, where signals from remote base stations (BSs) are usually very weak. Reconfigurable intelligent Surfaces (RISs) are expected to be part of future communication networks because of their capability to create a smarter controllable radio environment. In this paper, we study whether RIS can replace the function of a remote cell in the DL-TDOA measurement, hence maintaining the localization procedure fully within a single cell. We consider a scenario with one BS and one RIS, and show that the TDOA between the line-of-sight path and the reflected path through the RIS can replace the DL-TDOA measurement in the 3GPP NR recommendations. The DL-TDOA and the time-of-flight measurements between the BS and the UE suffice to accurately localize the UE. The proposed algorithm uses one round trip time (RTT) observation and one TDOA observation in millimeter wave (mmWave) frequencies. We present an extended Kalman filter positioning and tracking algorithm to localize users. Simulation results show that the positioning accuracy of RIS-enabled localization matches that of the two-cell structure while being a cost-effective solution.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".