Cooperative Communication Using Bit-Selective Adaptive Demodulation and Raptor Codes: The Gaussian Relay Channel Case
Bibliographic record
Abstract
In this paper, we propose a cooperative communication system where every link adapts to the channel condition without any feedback, by using a recently proposed adaptive demodulation (ADM) scheme. In ADM, the receiver demodulates only a subset of the bits from a Raptor coded Gray mapped M-QAM received symbol, where the number of demodulated bits depends on the channel condition. The ADM scheme, which was originally proposed for point to point communication, determines the most reliable bits using approximate decision regions obtained through a binary composite hypothesis test in the AWGN environment. In contrast, we prove a simple theorem that identifies the most reliable bits in a Gray mapped M-QAM received symbol in a straight forward and exact manner, and use it in every receiver of our cooperative communication system to adaptively extract the appropriate bits. Performance results demonstrate that our ADM based Raptor coded cooperative communication system is robust enough to realize diversity order of two, even under adverse channel conditions in various links.
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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) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".