A Novel Coordinated Multipoint Scheme With Zero Guard Interval for ATSC 3.0 Single Frequency Networks
Bibliographic record
Abstract
Advanced Television Systems Committee (ATSC) has issued ATSC 3.0 as the standard for the next-generation of Digital Terrestrial Television (DTT) broadcasting. ATSC 3.0 introduces several new features including Layered Division Multiplexing (LDM) that is a form of Non-Orthogonal Multiple Access (NOMA). ATSC 3.0 supports the legacy Single Frequency Networks (SFN), where the Guard Interval (GI) is considered to overcome the multipath as well as the asynchronous reception from different transmitters. Considering the huge distances between the DTT broadcasters, the GI overhead could be comparable to the data size, making the control/data ratio unfavorable. This paper proposes the association of Coordinated MultiPoint (CoMP) in SFN with NOMA-LDM. A novel formation of the channel matrix is provided that jointly includes the asynchronous channel's cross correlations for further joint detection of the signals at the receiver. The purpose is to enhance the coverage and spectral efficiency while avoiding hectic guard intervals or directional antennas. The capacity region is derived in an information theoretic framework based on exploiting the asynchronous channels' memory and correlation. We show that the spectral efficiency can exceed that of the non-coordinating schemes, provided that the proper receiver is equipped with the proposed channel matrix formation. Our extensive simulations validate that our proposed association of CoMP-SFN with NOMA-LDM provides a considerable boost in the coverage and channel reliability, while increasing the spectral and power efficiency.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".