LDM in Wireless In-Band Distribution Link and In-Band Inter-Tower Communication Networks for Backhaul, IoT and Datacasting
Bibliographic record
Abstract
Last year the authors presented a paper on full backward compatible ATSC 3.0 in-band backhaul for SingleFrequency-Network (SFN) using Layered-Division-Multiplexing (LDM) [1]. A complete in-band approach that provides backhaul for both robust mobile and high-data-rate fixed services was proposed. This paper continues the study with detailed analysis of the backhaul issues and implementation considerations. The field measurement results that support the viability of the inband backhaul system implementation using full-duplex transmission are presented. A full duplex Inter-Tower Communication (ITC) System - a scalable and re-configurable wireless network for SFN broadcasting, in-band inter-tower communications, and IoT/datacasting applications, is proposed. The ITC network uses LDM transmission to carry STL data alongside broadcast data intended for public reception. It offers the possibility of delivering backhaul data for future applications over the DTV infrastructure, such as IoT and connected vehicles. It is one enabling technology to achieve convergence of broadcast services with broadband and other wireless services.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.003 | 0.001 |
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".