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Record W2507895428 · doi:10.1049/iet-com.2016.0048

Area spectral efficiency of infrastructure relay enhanced cellular systems

2016· article· en· W2507895428 on OpenAlexaff
Jun Zhu, Lei Zhang, Hong‐Chuan Yang, Mazen O. Hasna

Bibliographic record

VenueIET Communications · 2016
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of VictoriaUniversity of British Columbia
Fundersnot available
KeywordsSpectral efficiencyRelayComputer scienceTelecommunications linkRayleigh fadingTransmission (telecommunications)Transmitter power outputElectronic engineeringBandwidth (computing)Relay channelComputer networkSingle antenna interference cancellationFadingChannel (broadcasting)TelecommunicationsTransmitterPower (physics)EngineeringPhysics

Abstract

fetched live from OpenAlex

In this study, the authors investigate the downlink area spectral efficiency (ASE) of infrastructure relay enhanced cellular systems. ASE for cellular systems is defined as the maximum achievable data rate per unit bandwidth per unit area supported by a base station. In relay enhanced systems, ASE can serve as an attractive performance metric by capturing the benefit of the relatively smaller spatial footprint of relay transmission and the resulting lower co‐channel interference. In this study, by applying a moment generating function based approach, the authors first derive the general closed‐form statistics of the total interference from dominant co‐channel cells over Rayleigh fading channels. These interference statistics are readily applied to the calculation of the resulting ASE. The authors also propose a novel in‐cell frequency reuse scheme to further exploit the smaller transmission power of relay stations (RSs). Through selected numerical examples, the authors show that in relay enhanced systems, the cell size, transmission power and RS positions should be properly selected to gain better ASE performance than the conventional systems without relays.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.948
Threshold uncertainty score0.770

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0040.001
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 head0.028
GPT teacher head0.262
Teacher spread0.234 · 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 designBench or experimental
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

Citations0
Published2016
Admission routes1
Has abstractyes

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