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Record W2150647207 · doi:10.1109/icc.2011.5963212

Throughput-Optimal Half-Duplex Cooperative Scheme with Partial Decode-Forward Relaying

2011· article· en· W2150647207 on OpenAlexaff
Ahmad Abu Al Haija, Mai Vu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsMcGill University
Fundersnot available
KeywordsAdditive white Gaussian noiseComputer scienceDecodesChannel (broadcasting)ThroughputDecoding methodsComputer networkGaussianWirelessTelecommunications

Abstract

fetched live from OpenAlex

We study a cooperative communication system consisting of two users in half duplex mode communicating with one destination over additive white Gaussian noise (AWGN). Cooperation is performed between the two users by partial decode-forward relaying over 3 time slots with variable duration. During the first two slots, each user alternatively transmits and partially decodes while during the last time slot, both users cooperate to forward information to the destination. We establish the achievable rate region of this scheme. Then using the Lagrangian method, we analyze the optimal power allocations and the optimal time duration that maximize its individual and sum rates for the symmetric channel. Results show a significant improvement of the rates compared to the classical multiple access channel (MAC) when the inter-user channel quality is better than that between each user and the destination.

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)0.002
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: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.073
GPT teacher head0.279
Teacher spread0.206 · 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
GenreMethods

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

Citations5
Published2011
Admission routes1
Has abstractyes

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