Constellation Rotation for DVB Multiple Access Channels With Raptor Coding
Bibliographic record
Abstract
In this paper we investigate a technique for increasing the capacity of a Digital Video Broadcasting-Return Channel via Satellite (DVB-RCS) system. In our previous work, we proposed a scheme consisting in adding a non-orthogonal interfering source to a DVB-RCS channel carrying data from an existing source. The inclusion of the new source does not cause bandwidth increase. The secondary source uses Raptor code. The destination will use successive decoding. It decodes the interfering source first, and after its removal, decodes the main source. In this paper, we generalize our work to allow possibility of decoding either the secondary source data (as in our previous work) or the main source data first. We investigate the performance and delay for each decoding scheme. Since the channels are non-orthogonal, it is possible that for some power allocation scenarios constellation points get erased. To address this problem, we use constellation rotation. The constellation map of the secondary source is rotated to increase the average distance between the points in the constellation resulting from the superposition of the main and interfering sources' constellations. Finally, we determine the optimum constellation rotation angle for the interfering source analytically and confirm it with simulations.
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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 | 0.001 | 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".