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Record W2745123784

Channel Simulators for MmWave and 5G Applications

2017· other· en· W2745123784 on OpenAlexaboutno aff
Fang Qiu

Bibliographic record

VenueTampere University Institutional Repository (Tampere University) · 2017
Typeother
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsChannel (broadcasting)Computer scienceTelecommunicationsComputer networkElectronic engineeringEngineering
DOInot available

Abstract

fetched live from OpenAlex

Along with the tremendous growth of extremely high traffic demand, 5G radio access technology, is becoming the core component to support massive and multifarious connected devices and real-time, and to offer high reliability wireless communications with high data rate. And millimeter-wave (mmWave) range with a huge frequency spectrum from 3 GHz to 300GHz will perfectly meet the multi-gigabit communicative demand. However, mmWave usage also generally brings new challenges, such as coping with high attenuation or path losses.
\nAs an effective method to evaluate the performance of the new concept in communication networks, nowadays, several channel models and simulators have been proposed and developped, such as, WINNER, COST-2100, IMT-Advanced, METIS, NYU Wire-less and QuaDRiGa etc. The thesis goals have been to offer an overview of the advantages and disadvantages of various mmWave channel models existing in the literature, based on the published literature, and to compare based on simulations some of the main features of two selected open-source models, namely the WINNER 2 and QuaDRiGa channel models. In the future, more mmWave channel models are planned to be tested and simulated for a better understanding of their suitability for various mmWave applications.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.438
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
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 head0.012
GPT teacher head0.182
Teacher spread0.170 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2017
Admission routes1
Has abstractyes

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