Bibliographic record
Abstract
This thesis introduces a new rate-adaptive system, Adaptive Demodulation (ADM), in which the receiver demodulates only those bits that have a high probability of being correct, treating non-demodulated bits as erasures. Several sets of decision regions, derived using composite hypothesis testing, are proposed for 16-QAM and 16-PSK which allow for the simple implementation of this demodulation strategy. The optimality of the proposed decision regions in selecting the most likely subset of bits from any received symbol is proven. It is demonstrated that encoding the data with a Luby Transform (LT) code allows for simple reconstruction of the message regardless of the erasure pattern introduced from the non-demodulated bits. Also demonstrated is the strong performance of 16-QAM for this application compared to other power efficient constellations and the near-optimality of using Gray mapping even under the proposed alternate sets of decision regions. The adaptive demodulation methodology is extended to the differentially coherent demodulation of 16-DPSK and 16-DAPSK, for which optimal demodulation strategies are derived. A methodology is presented for the receiver to effectively choose its operating region based on the observed instantaneous signal to noise ratio (SNR) at the receiver. Techniques for designing the degree distributions of the LT code used with the system are presented and analyzed for a simple binary message passing decoder, a ternary message passing decoder, a more general min-sum decoder, and a full belief propagation decoder. It is shown that for LT codes operating over binary symmetric channels, the min-sum decoder has performance identical to the ternary decoder. Numerical results for the spectral efficiency of the ADM system for both uncoded and coded bits are presented and compared to the performance of current rate-adaptive systems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".