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Record W4206838038 · doi:10.36227/techrxiv.12543167.v1

Performance Analysis of Wireless Mesh Backhauling Using Intelligent Reflecting Surface

2020· preprint· en· W4206838038 on OpenAlexaff
Mohammad Al‐Jarrah, Arafat Al‐Dweik, Emad Alsusa

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsWestern University
Fundersnot available
KeywordsRician fadingWirelessComputer scienceArchitectureTopology (electrical circuits)Wireless mesh networkChannel (broadcasting)Computer networkFadingWireless networkMathematicsTelecommunicationsCombinatoricsGeography

Abstract

fetched live from OpenAlex

This paper considers the deployment of intelligent reflecting surfaces (IRSs) technology for wireless multi-hop backhauling of multiple basestations (BSs) connected in a mesh topology. The performance of the proposed architecture is evaluated in terms of outage and symbol error probability in Rician fading channels, where closed-form expressions are derived and demonstrated to be accurate for several cases of interest. The analytical results corroborated by simulation, show that the IRS-mesh backhauling architecture has several desired features that can be exploited to overcome some of the backhauling challenges, particularly the severe attenuation at high frequencies.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.301
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.094
GPT teacher head0.333
Teacher spread0.239 · 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 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

Citations4
Published2020
Admission routes1
Has abstractyes

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