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Record W2169636820 · doi:10.1109/twc.2006.04598

Cooperative transmission in poisson distributed wireless sensor networks: protocol and outage probability

2006· article· en· W2169636820 on OpenAlexaff
Liang Song, Dimitrios Hatzinakos

Bibliographic record

VenueIEEE Transactions on Wireless Communications · 2006
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceComputer networkCooperative diversityWireless sensor networkNode (physics)Upper and lower boundsRelayTransmission (telecommunications)Wireless networkTopology (electrical circuits)Poisson distributionWirelessKey distribution in wireless sensor networksPower (physics)TelecommunicationsMathematicsEngineeringStatisticsPhysics

Abstract

fetched live from OpenAlex

We study cooperative wireless communications in the physical layer of a Poisson distributed wireless sensor network, where the spatial diversity of multiple relay nodes is utilized to improve the link performance. The tradeoff among network power consumption, spectral efficiency, outage probability, and sensor node density is discussed under the proposed cooperative transmission protocol for sensor networks (CTP-SN). CTP-SN is considered as a typical implementation of the two-phase cooperative transmission paradigm in wireless sensor networks. We derive an asymptotic upper bound for the capacity outage probability of CTP-SN. The bound is shown to be decreasing exponentially, when the sensor node density increases. Via the bound, we demonstrate that the cooperative protocol performs asymptotically much better than the non-cooperative direct transmission

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)0.005
metaresearch head score (Gemma)0.022
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: none
Teacher disagreement score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.003
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0020.001
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 head0.033
GPT teacher head0.291
Teacher spread0.257 · 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

Citations33
Published2006
Admission routes1
Has abstractyes

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