Raptor-network coding strategies for energy efficient cooperative DVB-H multimedia communications
Bibliographic record
Abstract
Reliable and energy-efficient delivery of mobile multimedia across different platforms and application scenarios is very challenging. Both open and proprietary standards exist worldwide; we focus on enhancements to the digital video broadcasting - handheld (DVB-H) standard for transmission over cooperative cellular networks, which enables the use of relaying to supplement existing fixed infrastructure. Cooperation creates a distributed form of multi-input-multi-output (MIMO) systems, resulting in breakthroughs in network energy efficiency and reliability. Thus, cooperative networks have an inherently green ad hoc topology that is able to substantially reduce infrastructure cost, system complexity and energy expenditure. In this paper, novel strategies are proposed and evaluated that combine: (1) fountain coding, e.g., Raptor codes at the application layer, and (2) network coding. Originally proposed for broadcasting over the Internet, the application of fountain codes to wireless cooperative communications networks has been limited to date. These codes enable lower (physical) layer compatibility. Network coding is used to reduce energy consumption by opportunistically recombining and rebroadcasting required combinations of packets. The energy cost of our peer-to-peer file repair sessions, which are integrated with the file delivery session itself, are quantified. Signal processing and modeling techniques used for cross-layer system simulations are also overviewed.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".