Raptor Coding for Non-Orthogonal Multiple Access Channels
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Bibliographic record
Abstract
In this paper, we propose a scheme for increasing the capacity of a communication channel by overloading a pre-existing channel with a controlled interfering channel using Raptor code. Combination of Raptor code and use of interference cancellation in the resulting Multiple Access Channel (MAC) results in almost perfect removal of the effect of the interfering channel. Thus, the primary channel's performance remains intact. As in the other MAC detection scenarios, for optimal performances, a power difference between the main and interfering transmitters is required. In the case that these two powers are equal, we propose a hard decision stage prior to the decoding at the destination in order to eliminate erased symbols. The proposed technique can also be used for increasing the capacity of forward and/or return links of the DVB Return Channel via Satellite (DVB-RCS).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it