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Record W2053818368 · doi:10.1109/glocom.2012.6503444

Phase asynchronous cognitive interference channels: Lossless source-channel separation theorems

2012· article· en· W2053818368 on OpenAlexaff
Hamidreza Ebrahimzadeh Saffar, Patrick Mitran

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Communication Security Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsLossless compressionComputer scienceCognitive radioChannel (broadcasting)Interference (communication)Asynchronous communicationEncoderCoding (social sciences)CognitionTransmission (telecommunications)Constraint (computer-aided design)Topology (electrical circuits)Computer networkTelecommunicationsAlgorithmMathematicsWirelessData compressionPsychology

Abstract

fetched live from OpenAlex

Sufficient and necessary conditions for reliable lossless communication of two correlated sources over classes of phase asynchronous cognitive interference channels are derived. Namely, we consider interference channels in which one of the encoders, i.e., the secondary or cognitive user, is causally or non-causally aware of the other's message. Moreover, as a practical constraint, we assume the transmitters are not aware of the phase shifts introduced by channels. We show that, for both classes of causal and noncausal cooperation, under strong interference conditions, separate source and channel coding is optimal for reliable communication of both users. Also, we derive necessary and sufficient conditions for reliable communication of the cognitive radio transmission while the primary is able to maintain the same information rate it could reliably send in the absence of the secondary. To the best of our knowledge, this is the first work to find fundamental limits of lossless reliable communication for cognitive interference channels.

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)0.003
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.003
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.029
GPT teacher head0.311
Teacher spread0.281 · 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 designTheoretical or conceptual
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

Citations3
Published2012
Admission routes1
Has abstractyes

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