<title>Rake receiver performance in the presence of narrowband jamming</title>
Bibliographic record
Abstract
Direct-sequence spread spectrum (DSSS) modulation offers many properties that make it well suited for a mobile environment including some inherent narrowband interference or jamming (NBJ) suppression capability and resistance to multipath fading. The estimation and filtering of unwanted narrowband signals in DSSS systems has been extensively addressed in previous work but has given limited insight to system performance when multipath fading is introduced and a diversity solution such as the ubiquitous Rake receiver is implemented. In this case, multiple correlators (or fingers) are used to extract the desired signal replicas from the individual delay path components. For the maximum ratio combiner (MRC) version of the Rake receiver, the signal replicas from each finger are then combined in some weighted sense to formulate the final decision threshold. The focus of this study is twofold: to investigate the inaccuracies incurred on path delay estimation due to the presence of NBJ and its impact on the system Bit Error Rate (BER). In order to reduce the impact of NBJ, some adaptive NBJ suppression filters are suggested.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".