Application of Raptor Coding With Power Adaptation to DVB Multiple Access Channels
Bibliographic record
Abstract
In this paper we propose a scheme to increase the channel capacity of Digital Video Broadcasting (DVB) systems which is also extendable to Return Channel via Satellite (DVB-RCS) scenarios. This increase is made possible by introduction of a new interfering channel to an exiting DVB channel. The interfering channel uses Raptor code. Through successive decoding in the destination, the data of main and interfering sources is decoded. We examine the case of sources with equal transmit power levels, however, as in all Multiple Access Channel (MAC) detection methods, there should be a power difference between the two sources to achieve higher rates. We demonstrate that when the power difference exists, there is a tradeoff between achieved rate and power efficiency and we will find the optimum power allocation scenario for this tradeoff. A power adaptation scheme is proposed that allocates the optimal power to the interfering channel based on an estimate of the main channel's condition. This estimate is obtained from the amount of overhead required by the destination for the successful decoding of the message. Therefore, the interfering source is able to adapt itself to the system without having any access to Channel State Information (CSI) of the main channel.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".