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Record W2033185214 · doi:10.1109/lcomm.2015.2472531

Capacity Bounds for a Class of Diamond Networks With Conferencing Relays

2015· article· en· W2033185214 on OpenAlexaff
Wanyao Zhao, David Yiwei Ding, Ashish Khisti

Bibliographic record

VenueIEEE Communications Letters · 2015
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceComputer networkUpper and lower boundsChannel capacityRelayWireless networkGaussianChannel (broadcasting)WirelessBackhaul (telecommunications)TelecommunicationsMathematics

Abstract

fetched live from OpenAlex

Diamond channels can model modern wireless communication infrastructure that use relays to increase network capacity. This letter investigates the effect of conferencing between the relays in a particular class of diamond networks with source-to-relay and conferencing channels modeled as noiseless bit-pipes of given capacities. A lower bound to the capacity for this network and an upper bound that is tighter than the cut-set bound are presented. Such networks with specific multiple access channel (MAC) types are studied and the minimum conferencing link capacity needed to achieve full cooperation for each MAC is ascertained. It is found that such minimum conferencing link capacity for the Gaussian MAC must be equal to the difference between the full cooperation capacity and the backhaul capacity. However, such a result does not hold in general as the binary adder MAC illustrates a counterexample.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.118
GPT teacher head0.291
Teacher spread0.173 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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