MétaCan
Menu
Back to cohort

Outage Performance of Downlink Coordinated Direct and Relay Transmission with NOMA over Nakagami-m Fading Channels

2020· article· en· W3133365493 on OpenAlexaff
Lve Han, Wei‐Ping Zhu, Min Lin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsConcordia University
Fundersnot available
KeywordsNakagami distributionNomaTelecommunications linkFadingRelayComputer scienceTransmission (telecommunications)Independent and identically distributed random variablesChannel state informationComputer networkElectronic engineeringChannel (broadcasting)WirelessAlgorithmTopology (electrical circuits)TelecommunicationsRandom variableMathematicsStatisticsEngineeringElectrical engineeringPower (physics)Physics

Abstract

fetched live from OpenAlex

In this paper, the outage performance of nonorthogonal multiple access (NOMA) based coordinated direct and relay transmission (CDRT) system is analyzed. Exact and asymptotic outage probabilities of both cell-center user (CCU) and celledge user (CEU) are derived. Our analysis is conducted based on the independent but not identically distributed (i.n.i.d) Nakagami-m fading model and the fixed-gain amplify-and-forward relaying protocol, and hence the result is applicable to general relay-aided communications, where only statistical channel state information is available. Monte Carlo simulation results are provided to verify the accuracy of the derived analytical expressions with comparison to conventional orthogonal multiple access (OMA) counterpart as well as the decode-and-forward NOMA-based CDRT, demonstrating the superiority of the proposed scheme.

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 score0.148
Threshold uncertainty score0.395

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.010
GPT teacher head0.195
Teacher spread0.185 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced Wireless Communication TechnologiesFrench-language works237,207