A Robust PN Code Tracking Algorithm for Frequency Selective Rayleigh-Fading Channels
Bibliographic record
Abstract
In this letter, we propose a new tracking scheme that is robust against multipath fading for pseudonoise (PN) code tracking in direct-sequence-spread spectrum systems. The proposed scheme employs an adaptive filter whose taps are adapted using a block least-mean square algorithm and it results in minimizing the effect of multipath interference on the tracking performance. We show that the mean-squared tracking error performance of the proposed scheme is not affected by the presence of closely spaced paths (e.g., one to three chips), unlike that of conventional delay locked loops. We also show that the tap-weight distribution of the filter provides accurate estimates of the multipath delays. For example, at E/sub b//N/sub 0/=5 dB, 98% of the time the path estimates lie within one sample (1/5 of a chip) from the actual delays. Furthermore, simulation results suggest that multipath delays over a wide range of terminal speeds can be tracked successfully. The proposed scheme is well suited for wideband code-division multiple-access systems where a large number of closely-spaced multipath components need to be tracked and used in RAKE combining.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".