Design and evaluation of a testbed for mobile TV networks
Bibliographic record
Abstract
This article presents the design of a complete, open-source, testbed for broadcast networks that offer mobile TV services. Although basic architectures and protocols have been developed for such networks, detailed performance tuning and analysis are still needed, especially when these networks scale to serve many diverse TV channels to numerous subscribers. The detailed performance analysis could also motivate designing new protocols and algorithms for enhancing future mobile TV networks. Currently, many researchers evaluate the performance of mobile TV networks using simulation and/or theoretical modeling methods. These methods, while useful for early assessment, typically abstract away many necessary details of actual, fairly complex, networks. Therefore, an open-source platform for evaluating new ideas in a real mobile TV network is needed. This platform is currently not possible with commercial products, because they are sold as black boxes without the source code. In this article, we summarize our experiences in designing and implementing a testbed for mobile TV networks. We integrate off-the-shelf hardware components with carefully designed software modules to realize a scalable testbed that covers almost all aspects of real networks. We use our testbed to empirically analyze various performance aspects of mobile TV networks and validate/refute several claims made in the literature as well as discover/quantify multiple important performance tradeoffs.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".