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Record W2022435419 · doi:10.1109/vetecf.2010.5594284

Multi-Hop Relay Networks with Multiple-Antenna Equipped Source and Destination

2010· article· en· W2022435419 on OpenAlexaff
Gayan Amarasuriya, Chintha Tellambura, Masoud Ardakani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRelayCumulative distribution functionHop (telecommunications)Diversity gainMoment-generating functionMonte Carlo methodComputer scienceCoding gainUpper and lower boundsTopology (electrical circuits)Outage probabilityProbability density functionTelecommunicationsMathematicsFadingStatisticsDecoding methodsCombinatoricsPhysicsMathematical analysis

Abstract

fetched live from OpenAlex

The performance of a multi-hop amplify-and-forward relay network is analyzed. The source and destination terminals are equipped with multiple-antennas and the relays with single-antennas. The cumulative distribution function and the moment generating function of two tight upper bounds of the end-to-end signal-to-noise ratio are derived. The lower bounds for the outage probability and the average symbol error rate (SER) are also derived. The results take into account the source-relay and the relay-destination correlation matrices; the uncorrelated case is treated as well. The asymptotic outage probability, average SER, diversity order and coding gain are also derived. Numerical results and Monte-Carlo simulations are presented to analyze the system performance and show the tightness of the proposed bounds.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.260
Teacher spread0.233 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

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