Signal Isolation in Full-Duplex Inter-Tower Communication Networks: Field Trials
Bibliographic record
Abstract
Future digital terrestrial television (DTT) systems, such as ATSC 3.0, are evolving to offer users new applications and services. These new applications (e.g., target advertisement, connected cars, 4k/8k video content) require a considerable increase in transmission capacity. In contrast, the current standards and technologies need to improve the offered spectral efficiency rate to adapt to the new use cases. A good alternative is to enable full-duplex communications among the ATSC transmission centers to build a mesh network of transmitters (i.e., Inter-Tower Communication Network or ITCN). The main drawback of this idea is the self-interference (SI) or loopback signal that is generated and added to the received signal. To reduce the impact of the SI, the transmission centers combine different signal isolation and cancellation techniques. This paper focuses on the characterization of the signal power isolation between the transmitted and the received signals under different conditions. In particular, this paper shows the results obtained during a measurement campaign in a real broadcast transmission center. The measurements were carried out following a novel signal isolation measurement methodology, and the results analyze the obtained signal isolation and the time variability of the received signal power. The results show that a minimum isolation value of 69.5 dB can be obtained for the worst evaluated case.
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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 | 0.004 | 0.004 |
| 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 | 0.001 |
| 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".