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Gaussian Broadcast Channels with Bidirectional Conferencing Decoders and Correlated Noises

2023· article· en· W4386075162 on OpenAlexaff
Reza K. Farsani, Wei Yu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Communication Security Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGaussianChannel (broadcasting)Upper and lower boundsDecoding methodsLambdaNoise (video)Computer scienceGaussian noiseTopology (electrical circuits)MathematicsTelecommunicationsAlgorithmComputer networkDiscrete mathematicsPhysicsCombinatoricsMathematical analysisImage (mathematics)OpticsArtificial intelligenceQuantum mechanics

Abstract

fetched live from OpenAlex

The two-user Gaussian broadcast channel (BC) with correlated noises and with decoders connected by cooperative links of finite capacities (known as conferencing decoders) is considered. A novel outer bound on the capacity region is established. For the channel with fully correlated noises (i.e., the noise correlation is either 1 or -1), the new outer bound yields exact capacity region for two cases: 1) BCs with degraded message sets; 2) BCs with one-sided conferencing from the weaker receiver to the stronger receiver. For these two cases, it is also shown that the outer bound is within half bits to the capacity region for arbitrary noise correlation. Furthermore, for the Gaussian BC with arbitrary noise correlation λ, we show that regardless of the capacities of conferencing links, a one-sided cooperative scheme (from the stronger user to the weaker one) based on decode-and-forward is sufficient to achieve the capacity region to within $\frac{1}{2}\log \left( {\frac{2}{{1 - |\lambda |}}} \right)$ bits.

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 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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.669
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.016
GPT teacher head0.226
Teacher spread0.209 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations2
Published2023
Admission routes1
Has abstractyes

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