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Record W2170056754 · doi:10.1109/tit.2007.904783

Multisource, Multidestination, Multirelay Wireless Networks

2007· article· en· W2170056754 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

VenueIEEE Transactions on Information Theory · 2007
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDecoding methodsComputer networkComputer scienceRelayMulticastWireless sensor networkFadingTransmitterLinear network codingCoding (social sciences)Node (physics)AlgorithmMathematicsEngineeringNetwork packetChannel (broadcasting)

Abstract

fetched live from OpenAlex

Networks with multiple source–destination pairs, involving possibly multicast, and where there are multiple nodes that can serve as potential relay nodes, are considered. A multisource, multirelay coding scheme is developed. In this scheme, each source's information is sent to its destination nodes via a multirelay route, with the multiple multirelay routes operating concurrently even when they intersect with each other, in the same spirit as code-division multiple access (CDMA). It is found that in the generalization to multiple sources, backward decoding achieves higher rates than sliding-window decoding. The routing structure where a joint backward decoding can be performed is characterized. The achievable rate region is found to combine aspects of both multiple relay and multiple access. Potential applications of this coding scheme to sensor networks are discussed. In particular, the exact capacity for the data downloading problem in sensor networks, where there are multiple sensor sources and one sink or collector node, is established for certain geometries when there is phase fading that is unknown to the transmitter.

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 categoriesnone
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 score0.983
Threshold uncertainty score0.736

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 communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.249
Teacher spread0.236 · 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