Cooperative Positioning with the Aid of Reconfigurable Intelligent Surfaces and Zero Access Points
Bibliographic record
Abstract
Due to their capability in creating a controllable wireless environment, extending coverage and improving localization accuracy, reconfigurable intelligent surfaces (RISs) are expected to be a main component of future 6G networks. In this paper, we present a novel cooperative positioning (CP) use-case of the RIS in mmWave frequencies. We show that two mobile stations (MSs) are able to estimate their positions through device-to-device (D2D) communications, and processing the signals reflected from the RIS. We start by building the system model based on the uniform linear array (ULA) architecture of the RIS elements. Then, we derive the Fisher information matrix (FIM) and the Cramér-Rao lower bound (CRLB) for calculating the MSs positioning error. After that, we optimize the RIS configuration to minimize the CRLB. Finally, simulation results compare the localization performance of random phases at the RIS with the optimal configuration.
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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) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".