Quasi-Optimal Subcarrier Selection Dedicated for Localization With Multicarrier-Based Signals
Bibliographic record
Abstract
The objective of this paper is to design dedicated probing signals for localization based on orthogonal multicarriers that attain the lowest value of the possible fundamental limits. The proposed scheme named Quasi-Optimal Subcarrier Selection (QOSS) attempts to generate the probing signals in positioning systems by minimizing the Cramer-Rao lower bound of range estimation in nonoverlapping multipath channels instead, which do indirectly depress the performance bounds in overlapping multipath channels to a certain extent. Based on the optimization of power allocation on orthogonal subcarriers, two kinds of QOSS signals are proposed for: 1) basestation (BS)-based localization networks where mobile station (MS) transmits to all BSs, and 2) MS-based localization networks where multiple BSs simultaneously transmit to the MS. Two adaptive search algorithms have been presented to generate the QOSS signal for MS-based localization networks. The fundamental limits of both QOSS signals are theoretically derived, the close relationship between the bound of range estimation and that of localization is disclosed and the lower bound of position dilution of precision is further obtained. Numerical results and experiments demonstrate our proposed theory.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".