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Record W2977160802 · doi:10.1109/lcomm.2019.2942919

Coverage Analysis of Cooperative NOMA in Millimeter Wave Networks

2019· article· en· W2977160802 on OpenAlexafffund
Khagendra Belbase, Chintha Tellambura, Hai Jiang

Bibliographic record

VenueIEEE Communications Letters · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNomaRelayDecoding methodsComputer scienceComputer networkTransmission (telecommunications)Selection (genetic algorithm)Set (abstract data type)TelecommunicationsAlgorithmTelecommunications linkPower (physics)Artificial intelligence

Abstract

fetched live from OpenAlex

This letter analyzes the coverage probability of a cooperative non-orthogonal multiple access (NOMA) millimeter wave (mmWave) network consisting of source (S) and destination (D) pair without a direct link. A cooperative relay (R), which is selected from a set of active users, helps D to receive its data. The transmission from S to R is based on NOMA, by which R can receive its own data and D's data simultaneously. Two relay selection schemes are developed. For each scheme, the favorable users that meet a rate threshold are selected to be in the decoding set. Then from the decoding set, a relay is selected that is nearest to S (in the 1st selection scheme), or nearest to D (in the 2nd selection scheme). For both schemes, we characterize the spatial density (location dependent) of decoding sets and derive the coverage probability. Both schemes outperform orthogonal multiple access. We also derive the coverage of randomly picking a relay (i.e., no selection) to quantify the benefits of relay selection in mmWave NOMA.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.019
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 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

Citations9
Published2019
Admission routes2
Has abstractyes

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