Bayesian Cramer–Rao Bound, Extended and Unscented Kalman Filters Based Tracking Through Non-Ideal Transceivers in 5G and Beyond
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Bibliographic record
Abstract
This work investigates the tracking process and its performance in milliwave (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$mm$</tex-math></inline-formula>wave) systems implementing orthogonal frequency-division multiplexing (OFDM). We aim to track a single-antenna mobile station (MS) based on well-known pilots broadcast from a multiple-antenna base station (BS). We have a particular interest in the practical scenario where the MS and BS are equipped with hardware-impaired transceivers that distort the pilots. To this end, the extended Kalman-filter-based tracker (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EKFT</i>) and the unscented Kalman-filter-based tracker (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UKFT</i>) are proposed to accomplish the tracking process. We pay special attention in its design to the accuracy degradation caused by these hardware impairments (HWIs) as well as to the MS transition uncertainty. Afterwards, this work derives the performance analysis in the Bayesian Cramer-Rao bound (BCRB) term, which considers the information conveyed by the distorted pilot and the transition uncertainty model. Moreover, this analysis is not only for assessment purposes but also for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EKFT</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UKFT</i> design. Furthermore, this work enhances the tracking accuracy by adopting the Monte Carlo (MC) approach. Lastly, extensive computer simulation is conducted for a comprehensive discussion of the proposed tracker's performance and the related theoretical bound. The results present the harmful impact of HWIs, non-line of sight paths reflected of unknown scatterers, and clock offset on the tracking process and the capabilities of the proposed trackers in improving tracking accuracy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it