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LDM in Wireless In-Band Distribution Link and In-Band Inter-Tower Communication Networks for Backhaul, IoT and Datacasting

2020· article· en· W3138867850 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTelecommunications and Broadcasting Technologies
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsBackhaul (telecommunications)Computer networkComputer scienceWireless broadbandBroadcasting (networking)WirelessMultiplexingTelecommunicationsBroadbandWireless networkBase station

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.922
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.234
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it