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Record W2108313821 · doi:10.1109/isit.2010.5513258

Distortion exponents of two-way relaying cooperative networks

2010· article· en· W2108313821 on OpenAlexaff
Jing Wang, Jie Liang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsRelayExponentDistortion (music)Computer scienceBandwidth (computing)Topology (electrical circuits)GaussianTelecommunicationsSignal-to-noise ratio (imaging)Upper and lower boundsMathematicsPhysicsPower (physics)Mathematical analysisCombinatorics

Abstract

fetched live from OpenAlex

In this paper, we study the transmission of Gaussian signals in a three-node half-duplex bidirectional relaying network, where two users communicate in both directions with the help of one relay, and can transmit at different rates. The relay employs amplify-and-forward (AF) or decode-and-forward (DF) based cooperation protocols. We analyze the distortion exponent that characterizes the high signal-to-noise ratio (SNR) behavior of the end-to-end distortion of the reconstructed signal at each user node. The different rates of the two users necessitate the study of a new concept - the achievable distortion exponent region of the system. We first derive an outer bound on the distortion exponent region of two-way relaying communications, which is tight at large bandwidth ratio. We then obtain the optimal distortion exponent pairs of conventional one-way relaying strategies and AF/DF based two-way relaying protocols with single-rate coding. The results illustrate the effect of the bandwidth ratio and cooperation strategies on the optimal distortion exponents.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.967
Threshold uncertainty score0.324

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 science0.0010.000
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 head0.027
GPT teacher head0.282
Teacher spread0.255 · 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

Citations5
Published2010
Admission routes1
Has abstractyes

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