New Study of DTV Transmitter-Identification Sequence Capacity
Bibliographic record
Abstract
Digital Terrestrial Television (DTV) has been widely deployed world-wide in recent years. The transmitter identification (Tx-ID) technique specified in modern DTV standards becomes crucial nowadays as the number of DTV transmitters grows with the expanded coverage area. In the ATSC standards, Kasami sequences, an important family of pseudo random sequences, are adopted as the practical Tx-ID sequences since Kasami sequences posess the favorable properties of nearly impulse autocorrelation/cross-correlation functions and large sequence capacities. In this work, we would like to extend the existing study to address the corresponding Tx-ID reception quality in terms of received signal-to-interference ratio (RSIR) to various channel factors such as propagation fading and propagation decay rate along with Kasami correlational properties. Such in-depth RSIR study can help us to determine the capacity (the number) of effective Tx-ID sequences which can be utilized subject to a given transmitter-deployment topology, a given fading channel, and a given Tx-ID sequence-length. Extensive numerical experiments are also presented to illustrate the relationship between the capacity of Tx-ID sequences and aforementioned pertinent factors.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".