Cooperative Localization and Tracking Using RISs and Sidelink Communications
Bibliographic record
Abstract
Cooperative localization and tracking are expected to play a crucial role in supporting location-based services in 6G networks. This work shows that integrating reconfigurable intelligent surfaces (RISs) with sidelink communications between user equipments (UEs) can enhance tracking and localization accuracy in the absence of access points (APs). To achieve this, we consider a localization and tracking problem of RIS-assisted sidelink communications, where moving UEs are localized and tracked without relying on APs. Specifically, we first design orthogonal RIS phase shift vectors to separate RIS-aided (reflected) paths from direct sidelink communication paths at the receiving UE(s). The initial locations of the UEs are then derived from the estimated channel parameters, enabling the tracking of the UEs using an extended Kalman filter (EKF). We benchmark the performance of the localization with multiple RISs using the Cramér-Rao lower bound (CRLB), and we assess the EKF's performance using the root mean squared error (RMSE) metric. Simulation results indicate that the initial localization accuracy reaches the CRLB, and the EKF achieves an RMSE below 10 cm for 90% of the time.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".