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Record W4401687091 · doi:10.1109/tvt.2024.3441030

Closed-Form Blockage Probability for Relays-Assisted mmWave D2D Communications: Benefits of Using Relays

2024· article· en· W4401687091 on OpenAlexaff
Cunyan Ma, Xiaoya Li, Chen He, Jinye Peng, Z. Jane Wang

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2024
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsUniversity of British Columbia
FundersChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsOutage probabilityComputer scienceElectronic engineeringRelayComputer networkEngineeringElectrical engineeringFadingChannel (broadcasting)Power (physics)Physics

Abstract

fetched live from OpenAlex

One of the important challenges of millimeter wave (mmWave) device-to-device (D2D) communications is its vulnerability to obstacles. While relaying is a possible solution when the direct mmWave D2D link is blocked by obstacles, the selected relay link may also be blocked by dynamic blockers that are difficult to know in advance. This paper focuses on investigating the dynamic blockage effects in relays-assisted mmWave D2D communication systems, where we assume that D2D users can switch to other available relays when the current link is blocked. A closed-form expression for the probability of all available relays being simultaneously blocked for a pair of D2D users is derived, which reveals the effects of the density and position of relays on the blockage characteristics and system reliability. Additionally, a closed-form expression for the minimum relay density required to satisfy certain quality of service requirements is obtained accordingly, and the feasible positions for a relay to effectively reduce the blockage probability is found based on particle swarm optimization. To the best of our knowledge, this is the first work to theoretically analyze the dynamic blockage in relays-assisted mmWave D2D communications, which provides valuable insights for designing network parameters.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.756
Threshold uncertainty score0.914

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.052
GPT teacher head0.268
Teacher spread0.215 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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