Integrated Inter-Tower Wireless Communications Network for Terrestrial Broadcasting and Multicasting Systems
Bibliographic record
Abstract
This paper describes systems, devices, and methods to implement a bi-directional integrated inter-tower wireless communications network (IITWCN). The described technology can be implemented in combination with the Broadcast Core Network (BCN) in next generation broadcast eco-system and, therefore, support new business cases for broadcast operators such as the delivery of flexible datacasting services or support broadcast or point-to-point Internet services. The introduced bi-directional inter-tower communications network (ITCN) extends the previous unidirectional in-band distribution links (IDL) and adopts the on-channel repeater (OCR) as a simplified backhaul solution in single frequency networks (SFN). The concept of the coordinated ITCN is also presented, aiming at future broadcast Internet services. The ITCN provides a scalable and configurable network solution embedded in a broadcast system, which becomes independent from any non-broadcasting telecommunication infrastructure. The described technology partially relies on the infrastructure of the underlying broadcast/multicast network, using the allocated service channels without requiring additional frequency bands or a separate frequency band. The bi-directional inter-transmitter communication links are therefore referred to as integrated transmission links and the corresponding network as an integrated network.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".