MétaCan
Menu
Back to cohort
Record W2155654891 · doi:10.1109/twc.2009.091004

Characterization of fading on fixed wireless channels between 200 MHz and 2 GHz in suburban macrocell environments

2009· article· en· W2155654891 on OpenAlexafffund
Anthony Liou, Kyle N. Sivertsen, Pouyan Arjmandi, G. M. Viswanathan, B. Diallo, Sol Lancashire, David G. Michelson

Bibliographic record

VenueIEEE Transactions on Wireless Communications · 2009
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsBC Hydro (Canada)University of British Columbia
FundersBC Hydro
KeywordsMacrocellFadingComputer scienceWirelessRadio spectrumTelecommunicationsNon-line-of-sight propagationElectronic engineeringEnvironmental scienceEngineeringBase stationChannel (broadcasting)

Abstract

fetched live from OpenAlex

Growing use of point-to-multipoint fixed wireless networks to support network access and SCADA applications in suburban macrocell environments has prompted regulators to re-allocate various bands between 200 MHz and 2 GHz to such applications. Links in such networks are usually obstructed by buildings and foliage and are classified as non-line-of-sight. Although it is well-known that such links are susceptible to fading caused by windblown trees and foliage, most past efforts to characterize fading on such links have focused on frequency bands at 1.9 GHz and above. Here, we show how signal fading in the 220, 850 and 1900 MHz bands vary with both distance and time-averaged wind speed in a representative macrocell environment. Based upon time-series of received signal strength collected in a typical macrocell environment with moderate foliage at locations between 1 and 4 km from a transmitting site located 80 m above ground level, we show that fading on such links is relatively severe at 1.9 GHz but decreases rapidly as the carrier frequency decreases. We have expressed our results in the form of a first-order simulation model. Additional data will be required to estimate standardized model parameters that can be applied to a broad range of environments.

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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score0.943

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 head0.028
GPT teacher head0.236
Teacher spread0.208 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations3
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Transactions on Wireless CommunicationsSame topicMillimeter-Wave Propagation and ModelingFrench-language works237,207