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Record W2019319094 · doi:10.1109/iccnc.2014.6785332

Joint network channel fountain scheme for reliable communication in wireless networks

2014· article· en· W2019319094 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

Venue2014 International Conference on Computing, Networking and Communications (ICNC) · 2014
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsCommunications Research Centre CanadaÉcole de Technologie Supérieure
Fundersnot available
KeywordsLinear network codingComputer scienceComputer networkFadingFountain codeChannel (broadcasting)Wireless networkTransmitterBinary erasure channelRelayCoding (social sciences)Channel state informationWirelessDecoding methodsBit error rateTransmission (telecommunications)Data transmissionChannel capacityTelecommunicationsBlock codeConcatenated error correction code

Abstract

fetched live from OpenAlex

Joint network-channel coding (JNCC) has attracted significant interest recently for reliable data transmission over error-prone transmission channel. However, it appears that no fixed-rate channel coding is capable of driving the outage probability to zero without channel state information at the transmitter. In this paper we employ rateless coding and network coding for reliable communication in wireless relay networks. Specially we develop a scheme of joint network and fountain coding (JNFC), which can effectively combat the detrimental effect of wireless fading channel by seamlessly coupling fountain and network paradigms. Simulation results justify that our proposed JNFC has significant performance advantage over other schemes in a variety of metrics.

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)0.003
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 score0.939
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0040.002
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 head0.080
GPT teacher head0.310
Teacher spread0.230 · 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