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Record W2099225340 · doi:10.1109/allerton.2008.4797712

Cooperative strategies for the half-duplex Gaussian parallel relay channel: Simultaneous relaying versus successive relaying

2008· article· en· W2099225340 on OpenAlexaff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRelayGaussianChannel (broadcasting)Relay channelLinear network codingTopology (electrical circuits)Additive white Gaussian noiseCoding (social sciences)

Abstract

fetched live from OpenAlex

We consider the problem of cooperative communication for a network composed of two half-duplex parallel relays with additive white Gaussian noise. Two protocols, i.e., Simultaneous and Successive relaying, associated with two possible relay orderings are proposed. The simultaneous relaying protocol is based on dynamic decode and forward (DDF) scheme. For the successive relaying protocol, a Non-Cooperative Coding based on dirty paper coding (DPC) is proposed. We also propose a general achievable rate based on the combination of the proposed simultaneous and successive relaying schemes. The optimum ordering of the relays and hence the capacity of the half-duplex Gaussian parallel relay channel in low and high SNR scenarios is derived. In low SNR scenario, we show that under certain conditions for the channel coefficients the ratio of the achievable rate of the simultaneous relaying protocol, based on the DDF scheme, to the cut-set bound of the half-duplex Gaussian parallel relay channel tends to 1. On the other hand, as SNR goes to infinity, we prove that successive relaying protocol, based on the DPC scheme, asymptotically achieves the capacity of the network.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.974
Threshold uncertainty score0.999

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 studies0.0030.000
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.078
GPT teacher head0.310
Teacher spread0.232 · 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.

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

Citations11
Published2008
Admission routes1
Has abstractyes

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