User Positioning in mmW 5G Networks using Beam-RSRP Measurements and\n Kalman Filtering
Bibliographic record
Abstract
In this paper, we exploit the 3D-beamforming features of multiantenna\nequipment employed in fifth generation (5G) networks, operating in the\nmillimeter wave (mmW) band, for accurate positioning and tracking of users. We\nconsider sequential estimation of users' positions, and propose a two-stage\nextended Kalman filter (EKF) that is based on reference signal received power\n(RSRP) measurements. In particular, beamformed downlink (DL) reference signals\n(RS) are transmitted by multiple base stations (BSs) and measured by user\nequipmentn(UE) employing receive beamforming. The so-obtained BRSRP\nmeasurements are fed back to the BS where the corresponding\ndirection-of-departure are sequentially estimated by a novel EKF. Such angle\nestimates from multiple BSs are subsequently fused on a central entity into 3D\nposition estimates of UE by means of an angle-based EKF. The proposed\npositioning scheme is scalable since the computational burden is shared among\ndifferent network entities, namely transmission/reception points (TRPs) and\n5G-NR Node B (gNB), and may be accomplished with the signalling currently\nspecified for 5G. We assess the performance of the proposed algorithm on a\nrealistic outdoor 5G deployment with a detailed ray tracing propagation model\nbased on the METIS Madrid map. Numerical results with a system operating at 39\nGHz show that sub-meter 3D positioning accuracy is achievable in future mmW 5G\nnetworks.\n
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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 | 0.001 |
| 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".