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Record W2104453881 · doi:10.1109/isit.2012.6283566

Multiple access channel with various degrees of asymmetric state information

2012· article· en· W2104453881 on OpenAlexaff
Nevroz Şen, Fady Alajaji, Serdar Yiiksel, Giacomo Como

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Communication Security Techniques
Canadian institutionsQueen's University
Fundersnot available
KeywordsEncoderComputer scienceChannel state informationIndependent and identically distributed random variablesChannel (broadcasting)Characterization (materials science)Decoding methodsProcess (computing)State (computer science)Channel capacityAlgorithmComputer networkTelecommunicationsWirelessMathematicsRandom variable

Abstract

fetched live from OpenAlex

We consider the problem of reliable communication over multiple-access channels (MAC) where the channel is driven by an independent and identically distributed state process and the encoders and the decoder are provided with various degrees of asymmetric (noisy or partial) channel state information (CSI). Namely, we provide a single letter characterization for the capacity region when the encoders have access to non-causal asymmetric partial CSI and the decoder has complete CSI. When the encoders observe asymmetric noisy CSI with asymmetric delays and the decoder observes complete CSI, we provide a single letter characterization for the capacity region. Finally, we consider a cooperative scenario with common and private messages, with noisy CSIT and complete CSIR and provide a single letter expression for the capacity region. For the cooperative scenario, we also note that as soon as the common message encoder does not have access to CSI, then for any noisy CSIT and CSIR setup it is possible to obtain a single letter characterization for the capacity region.

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)0.005
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.022
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0010.003
Scholarly communication0.0030.005
Open science0.0020.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.241
Teacher spread0.222 · 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 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

Citations4
Published2012
Admission routes1
Has abstractyes

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