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Record W2515162970 · doi:10.1109/tvt.2016.2607159

On the Spectral Efficiency of Selective Decode-and-Forward Relaying

2016· article· en· W2515162970 on OpenAlexaff
Hamza Ümit Sökün, Halim Yanıkömeroğlu

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2016
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsCarleton University
Fundersnot available
KeywordsSpectral efficiencyTransmission (telecommunications)Computer scienceChannel (broadcasting)Modulation (music)Decoding methodsLink adaptationTransmitter power outputElectronic engineeringAlgorithmComputer networkTelecommunicationsFadingEngineeringTransmitterPhysics

Abstract

fetched live from OpenAlex

Multirelay cooperative relaying enables spatial diversity, often at the expense of spectral efficiency. To alleviate the loss in spectral efficiency due to half-duplex relaying and transmission over orthogonal channels, we propose a novel transmission scheme for selective decode-and-forward (DF) networks. In this scheme, we assume that destination may receive signals from transmitting nodes with different modulation levels. Particularly, we obtain a closed-form expression for both end-to-end (E2E) average error probability and spectral efficiency in such a scheme. Subsequently, using these closed-form expressions and average channel statistics, we perform joint optimization of power allocation and modulation level selection to maximize the E2E spectral efficiency while maintaining a target E2E average error probability and a set of transmit power constraints. Simulation results demonstrate that the transmission scheme proposed herein improves the E2E spectral efficiency significantly, in comparison with the conventional adaptive DF transmission 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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.869
Threshold uncertainty score0.278

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 science0.0010.000
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 head0.015
GPT teacher head0.241
Teacher spread0.226 · 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

Citations11
Published2016
Admission routes1
Has abstractyes

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