Coverage study of ATSC 3.0
Bibliographic record
Abstract
The ATSC 3.0 next generation digital TV standard adopts state-of-the-art coding and modulation, as well as the new Layered Division Multiplexing (LDM) technology in addition to the traditional TDM/FDM. It is a very flexible system capable of combining different services in one RF channel with different robustness and reception conditions. The coverage for ATSC 3.0 is very different from the legacy one-tower-one-coverage ATSC 1.0 system. With the new enabling technologies, the ATSC 3.0 can greatly increase the coverage/service areas, reduce the distance between co-channel assignments, and introduce local program insertion and targeted advertisement. This paper discusses the ATSC 3.0 coverage and co-channel interference issues, using LDM technology with different operating parameters. Similar to LTE, the ATSC 3.0 co-channel assignment could be reduced to two times of service coverage radius (2R). This means an improvement of the spectrum efficiency by up to 4 times in comparison with today's system. The deployment of Single Frequency Network (SFN) can further improve the coverage and reduce the interference.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".