P-order metric UWB receiver structures with superior performance
Bibliographic record
Abstract
The generalized Gaussian probability density function is shown to better approximate the probability density function of the multiple access interference in ultra-wide bandwidth systems than the Gaussian approximation and the Laplacian density approximation. Two ultra-wide bandwidth receiver structures based on this new approximation using a p-order metric receiver decision statistic are investigated for the detection of time-hopping ultra-wide bandwidth wireless signals in multiple access interference channels. The first receiver outperforms both the conventional matched filter ultra-wide bandwidth receiver and the soft-limiting ultra-wide bandwidth receiver when only multiple access interference is present in UWB channels. The second new receiver with adaptive limiting threshold outperforms the conventional matched filter ultra-wide bandwidth receiver, the soft-limiting ultra-wide bandwidth receiver, and the adaptive threshold soft limiting ultra-wide bandwidth receiver in all multiple access interference-plus-noise environments. In multipath channels, a new Rake receiver based on the porder metric receiver is proposed for signal detection. Mathematical analysis and numerical results show that this new Rake receiver can achieve larger signal-to-interference-plus-noise ratio than the standard matched filter Rake receiver when multipath components are resolvable in UWB channels.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".