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

2022· article· en· W4287847151 on OpenAlexaff
Sungjun Ahn, Jeongchang Kim, Seok-Ki Ahn, Sunhyoung Kwon, Sungho Jeon, David Gómez‐Barquero, Pablo Angueira, Dazhi He, Cristiano Akamine, Mats Ek, Sesh Simha, Mark Aitken, Zhihong Hong, Yiyan Wu, Sung-Ik Park

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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