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Record W2156419975 · doi:10.1109/wcnc.2007.338

Diversity Order Bounds for Wireless Relay Networks

2007· article· en· W2156419975 on OpenAlexaff
John Boyer, D.D. Falconer, Halim Yanıkömeroğlu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsCarleton University
Fundersnot available
KeywordsCodebookRelayComputer scienceOutage probabilityDiversity gainWirelessEncoding (memory)Coding (social sciences)Linear network codingUpper and lower boundsWireless networkAlgorithmComputer networkTelecommunicationsMathematicsDecoding methodsFadingArtificial intelligenceStatistics

Abstract

fetched live from OpenAlex

This paper derives bounds on the diversity order and high SNR probability of outage behavior of wireless relay networks with arbitrary link connectivity between cooperating terminals. Single and multiple antennas per terminal are considered. Two general codebook generation schemes are analyzed and compared. Common codebook generation involves the source and all relays sharing a common randomly generated codebook, and encompasses many practical encoding schemes, including both repetition coding and space-time coding. Independent codebook generation involves the source and all relays employing independent randomly generated codebooks, and provides an information theoretic lower bound on the probability of outage of all achievable codebook generation schemes. The results indicate that although independent codebook generation does not offer any diversity gain over common codebook generation, it can offer a significant probability of outage improvement under certain conditions.

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)0.021
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.038
GPT teacher head0.283
Teacher spread0.244 · 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

Citations10
Published2007
Admission routes1
Has abstractyes

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