Packet Retransmission Schemes and Trials for Broadcast Services in Mobile Scenarios
Bibliographic record
Abstract
With the escalating prevalence of datacasting, live streaming and high-quality video consumption on mobile devices, there arises an increasing demand for a cost-effective and reliable approach to transmit large volumes of such content to extensive audiences. While broadband mobile networks can increase capacity through denser base stations and higher frequencies, the linear pace of facility development makes it difficult to match the non-linear growth of the service throughput. Terrestrial Broadcast has proven itself to be significantly more efficient in transmitting popular video streams to mobile devices over a large area. However, due to its downlink-only nature, it falls short of delivering consistently reliable services. Hence, the convergence of terrestrial broadcast and broadband mobile networks has resurfaced as a pertinent topic for consideration. In this paper, terrestrial broadcast is adopted as the main pipe to transmit streaming services to mobile phones, with a 5th generation mobile communications (5G) new radio (NR) mobile carrier employed to provide complementary packet loss retransmission service, ensuring a seamless service experience. First, a cross-standard packet retransmission (CPR) scheme is proposed based on 5G broadcast and 5G NR Systems. Corresponding protocols and schemes are introduced, and a prototype system is realized. CPR is able to support delay-insensitive datacasting services very well, yet its higher layer convergence poses challenges for supporting delay-sensitive real-time services. To address this, a MAC-layer homogeneous packet retransmission (HPR) scheme is proposed. The basic principle is to utilize the carrier aggregation mechanism of 5G, modifying the protocols to enable one carrier to simulate broadcast while maintaining unicast in another carrier. In HPR, packet retransmission can be done at the MAC layer, reducing the retransmission delay to within 5 microseconds. Simulation and trial results are presented based on the proposed schemes.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".