Convolutionally Coded Transmission over Markov-Gaussian Channels: Analysis and Decoding Metrics
Bibliographic record
Abstract
It has been widely acknowledged that the aggregate interference at the receiver for various practical communication channels can often deviate markedly from the classical additive white Gaussian noise (AWGN) assumption due to various ambient phenomena. Moreover, the physical nature of the underlying interference generating process in such cases can lead to a bursty behaviour of the interfering signal, implying that it is highly likely that consecutive symbols are affected by similar noise levels. In this paper, we devise and analyze detection techniques, in conjunction with a convolution code, for such interference channels that possess non-negligible memory by considering optimum and sub-optimum decoding metrics. In particular the inherent memory in the noise process is modeled as a first-order Markov chain, whose state selects the variance of the instantaneous Gaussian noise, leading to a Markov-Gaussian channel model. Analytical expressions are obtained for the cut-off rate, which is an ensemble code parameter, and the bit error rate for a convolutionally coded system, that are subsequently employed for an extensive evaluation of the various metrics considered. Furthermore, the interleaving depth is considered as a design parameter and its effect on performance is analyzed over a range of noise scenarios.
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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 | 0.013 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".