MétaCan
Menu
Back to cohort
Record W1994070335 · doi:10.1109/twc.2012.060212111755

Blind Detection with Unique Identification in Two-Way Relay Channel

2012· article· en· W1994070335 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Wireless Communications · 2012
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRelayComputer scienceTransmission (telecommunications)Channel (broadcasting)Relay channelBit error rateNode (physics)AlgorithmPhase-shift keyingNoise (video)Gaussian noiseModulation (music)Signal-to-noise ratio (imaging)Likelihood-ratio testKeyingTopology (electrical circuits)TelecommunicationsPower (physics)MathematicsStatisticsArtificial intelligence

Abstract

fetched live from OpenAlex

This paper considers the blind detection for a two-way relay system in which two source nodes exchange information via a relay node by amplify-and-forward relaying. An efficient transmission scheme is first proposed to achieve unique identifications of both the transmitted symbols and channel coefficients at a noise-free receiver using the M-ary phase shift keying modulation. Blind receivers based on the generalized likelihood ratio test are then derived for both the reciprocal and nonreciprocal channels with additive Gaussian noise. The least square error-based receiver is also studied for the case without prior knowledge of the noise power for detection. Moreover, constellation selection algorithms are proposed to achieve a uniform transmission bit rate for the ease of implementation. Finally, numerical results are provided to validate the proposed schemes.

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.

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)0.001
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 score0.952
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.053
GPT teacher head0.306
Teacher spread0.253 · 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