Using LDM to achieve seamless local service coverage in SFN environment
Bibliographic record
Abstract
This paper studies layered division multiplexing (LDM) for local cover age/services, such as location targeted advertisement or local content insertion. The LDM upper layer can be used to deliver time-division multiplexed (TDM-ed) mobile-HD and 4k-UHD services. The LDM lower layer with a negative SNR threshold can reliably provide seamless local coverage/service from each single frequency network (SFN) transmitter without coverage gaps among adjacent SFN transmitter service areas. No directional receiving antenna is required and receivers simply tune into the stronger local received signal. This is the concept of Cloud Transmission. In LDM system, the upper layer is operating in a traditional SFN mode to provide network-wide coverage. The lower layer is actually operating in a special form of Distributed MIMO or gap-filler mode to provide a targeted local coverage. Only Advanced Television Systems Committee (ATSC) 3.0 Baseline Technologies are used, i.e., there is no need to modify the ATSC 3.0 standard. Giving the upper and lower layers data rate requirements and the SNR thresholds, the lower layer injection level can be optimized for maximizing upper and lower layer performance and coverage.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".