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Record W2144575370 · doi:10.1109/glocom.2005.1577837

Cooperative diversity using message passing in wireless sensor networks

2005· article· en· W2144575370 on OpenAlex

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

VenueGLOBECOM '05. IEEE Global Telecommunications Conference, 2005. · 2005
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDecoding methodsDecodesRelayComputer scienceFadingCooperative diversityRelay channelChannel (broadcasting)Computer networkParity bitWirelessNode (physics)AlgorithmTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

Cooperative diversity schemes have been introduced in earlier works to achieve diversity in a block fading channel. However, most of these schemes ignore the quality of the source-relay (S-R) channel in the decoding process, even though it is this channel that limits the performance of cooperation schemes. This paper introduces a simple yet robust scheme for cooperative diversity based on message passing in the decoding process, which accounts for the quality of the S-R channel. In our scheme, the relay decodes the source symbols and forms parity bits, which are in turn used by the destination (D) to decode the source message. By accounting for the reliability of the parity bits, performance measures, such as bit error rate, are not limited by the S-R channel quality and improve with increasing signal-to-noise ratio on the S-D and R-D channels. With our scheme, feedback signals to the source node are not required, with only simple decoding and encoding required at the relay.

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), Science and technology studies
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 score0.817
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0050.003
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 head0.058
GPT teacher head0.300
Teacher spread0.242 · 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