DVB-S2 Satellite Experiment Results
Bibliographic record
Abstract
With a large number of profiles and options, the DVB-S2 standard offers several possibilities to move towards the Shannon capacity bound and more in general to reduce the delivery cost per bit in real system scenarios, compared to previous generations of air interface. The information bit delivery cost with high quality of service is a key factor of success for both broadcast and interactive high throughput satellite systems. This paper presents the main results and findings of extensive European Space Agency (ESA) funded DVB-S2 test campaigns performed by satellite on three complementary test platforms, addressing broadcast, professional and interactive profiles. Different satellite transponders operating in C-band, Ku-band and Ka-band and located in Europe and in Canada have been used encompassing both single beam and multi beam satellite coverage. The performance of high order modulations has been measured, as well as the effects of payload impairments with and without mitigation techniques such as modulator pre-distortion and receiver equalisation, the impact of phase noise, etc. Particular effort has been dedicated to investigate the performance of Adaptive Coding and Modulation (ACM) in real and emulated conditions. The paper also provides recommendations concerning different tradeoffs to be made when operating DVB-S2 carriers, like optimization of ACM margins, amplifier back-off setting and insertion of pilot symbols.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".