Would Future mmWave Wireless Networks Be an Alternative Positioning Technique to GNSS-Based High Precision Positioning?
Bibliographic record
Abstract
5G small cells have the potential to enable sub-meter positioning accuracy in urban canyons and downtown areas, where global navigation satellite system (GNSS) precise point positioning (PPP) suffers the most. As 5G is expected to have a dense deployment of base stations (BSs), it became imperative to utilize the extra information available by means of sensor fusion. Traditionally, an extended Kalman filter (EKF) is used for such a purpose. Yet, one of its main drawbacks is that it requires a linear relationship between the states and the measurements to ensure its optimality. Many papers in the literature perform multi-BS hybrid positioning through the fusion of raw range-based and angle-based measurements via an EKF. Such measurements are inherently highly non-linear with respect to the estimated position state, which leads to high linearization errors. In this paper, we first propose the integration of the available BSs on the positioning level instead of the integration on the raw measurement level to avoid the linearization errors of the EKF. Additionally, we propose a dynamically tuned covariance matrix (DTCM)-KF method, where the BSs are weighted based on their proximity to the UEs, with BSs further away weighted less. The proposed method was tested using a quasi-real setup based on a highway trajectory in Toronto, Canada, along with a ray-tracing-based 5G simulator. The potential of using the proposed 5G positioning as an alternative to GNSS-based positioning in urban canyons is investigated through the comparison with the GPS PPP. The results show that the proposed method outperforms traditional EKF-based measurements level fusion methods. Moreover, it is able to outperform the GPS-only PPP solution. The RMS, maximum, and 95% errors of the proposed method were found to be 0. 39m, 1.4m, and 0. 74m respectively.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".