On Burst Transmission Scheduling in Mobile TV Broadcast Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In mobile TV broadcast networks, the base station broadcasts TV channels in bursts such that mobile devices can receive a burst of traffic and then turn off their radio frequency circuits till the next burst in order to save energy. To achieve this energy saving without scarifying streaming quality, the base station must carefully construct the burst schedule for all TV channels. This is called the burst scheduling problem. In this paper, we prove that the burst scheduling problem for TV channels with arbitrary bit rates is NP-complete. We then propose a practical simplification of the general problem, which allows TV channels to be classified into multiple classes, and the bit rates of the classes have power of two increments, e.g., 100, 200, and 400 kbps. Using this practical simplification, we propose an optimal and efficient burst scheduling algorithm. We present theoretical analysis, simulation, and actual implementation in a mobile TV testbed to demonstrate the optimality, practicality, and efficiency of the proposed algorithm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it