MétaCan
Menu
Back to cohort
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 OpenAlex
Liang Song, Dimitrios Hatzinakos

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.001
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.961
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.033
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