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Record W2522155271 · doi:10.1109/spects.2016.7570506

Evaluation of capacity and power efficiency in millimeter-wave bands

2016· article· en· W2522155271 on OpenAlexaff
Abdulbaset M. Hamed, Raveendra K. Rao

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsWestern University
Fundersnot available
KeywordsNon-line-of-sight propagationComputer scienceElectronic engineeringRadio spectrumEfficient energy useMonte Carlo methodSpectral efficiencyChannel capacityChannel (broadcasting)Extremely high frequencyWirelessTransmission (telecommunications)MicrowaveTelecommunicationsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Millimeter wave (mmWave) spectrum bands have been proposed for commercial wireless communications to relieve the spectrum crunch in the microwave band. The mmWave bands are being vigorously pursued for multiple gigabit data transmission. In this paper, channel models for line-of-sight (LOS) and non-line-ofsight (NLOS) links for specific mmWave frequency bands are presented and then used in the evaluation of green efficiency metrics, maximum achievable capacity, bits/s, and power efficiency, bits/s/Thermal Noise Energy Unit. These efficiency indexes are investigated and illustrated using Monte Carlo simulation as a function of signal to noise ratio, channel model parameters and transmitterreceiver separation distance. The results show that the mmWave bands provide better channel capacity; however, less energy efficiency is achieved.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.055
GPT teacher head0.242
Teacher spread0.187 · 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

Citations16
Published2016
Admission routes1
Has abstractyes

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