Bibliographic record
Abstract
The frequency bands used by secondary systems such as cognitive radio (CR) and ultra-wideband (UWB) systems are subject to various forms of non-Gaussian noise and interference including co-channel interference caused by the primary user and other secondary systems and man-made impulsive noise. For secondary systems employing the popular combination of bit-interleaved coded modulation (BICM) and orthogonal frequency division multiplexing (OFDM) it has been recently shown that robustness against the negative effects of non-Gaussian impairments can be achieved by replacing the conventional L2-norm metric for Viterbi decoding with an Lp- norm metric. However, in order to achieve high performance, the metric parameter p has to be optimized for the underlying type of noise. In this paper, we propose two adaptive algorithms for online optimization of p. The first algorithm is based on a maximum-likelihood parameter estimation framework. The second algorithm is based on the direct minimization of the asymptotic bit error rate of Lp-norm decoding. Simulation results show that both algorithms have excellent performance and that the resulting adaptive Lp-norm metric outperforms other popular metrics in non-Gaussian noise 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".