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Empirical Modeling of UHF Wireless Channel in HPHT SFNs: Based on Seoul Metropolitan Case

2022· article· en· W4287847151 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

Venue2022 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB) · 2022
Typearticle
Languageen
FieldEngineering
TopicTelecommunications and Broadcasting Technologies
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsUltra high frequencyFadingChannel soundingComputer scienceChannel (broadcasting)TransmitterSingle-frequency networkElectronic engineeringWirelessTransmission (telecommunications)Computer networkTelecommunicationsMIMOEngineering

Abstract

fetched live from OpenAlex

This paper proposes realistic channel models to describe the fading effects in high-power high-tower (HPHT) single-frequency network (SFN) environments. The proposed models empirically characterize the ultra-high frequency wireless channels based on field data obtained from an operational SFN in a metropolitan area. To this end, large-scale channel sounding experiments are conducted by leveraging on-air transmitter identification signals. The unique features of HPHT SFN transmission are identified and reflected in the tapped delay line parameter definitions. Dedicated models are built for stationary and mobile environments so that they could relevantly assist network planning, system implementation, and performance test in the industry. Free MATLAB source code of the fading simulator is available at https://github.com/ETRI-KMOU/FadingChannelSimulator.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.035
GPT teacher head0.278
Teacher spread0.243 · 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