Soft-decoding based vector quantization for hidden-Markov channels
Bibliographic record
Abstract
Summary form only given. Channel optimized vector quantization (COVQ) has received considerable attention as an approach to joint source-channel coding. COVQ for hidden Markov channels was studied, with application in wireless communication over fading channels. The optimal VQ decoding was considered for finite state channels when the channel state is not explicitly observed at the receiver. A hidden Markov model (HMM) with a continuous observation space is constructed for the channel by assuming both channel input and channel state are first-order Markov process. A recursive-decoding algorithm is derived for computing the minimum mean square error (MMSE) optimal estimate for the source vector, based on the observed channel output sequence. In simulation experiments, the performance of a communications system based on the proposed quantizer is investigated for a Gauss-Markov signal source and a frequency non-selective Rayleigh fading channel. The results indicate that for fading channels, proposed soft-decoding based COVQ can results in a substantial improvement of performance over detection based COVQ.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.005 | 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".