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Record W3149942371

Transmission scheme and performance analysis for decode-and-forward MIMO two-way relay systems

2009· article· zh· W3149942371 on OpenAlexaff
Yang Lu

Bibliographic record

Venue中国科学(F辑:信息科学)(英文版) · 2009
Typearticle
Languagezh
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsConcordia University
Fundersnot available
KeywordsRelayChannel state informationTransmission (telecommunications)MIMOComputer scienceQuantization (signal processing)AlgorithmTransmission systemRelay channelBeamformingChannel (broadcasting)Control theory (sociology)MathematicsTopology (electrical circuits)TelecommunicationsWirelessArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

In decode-and-forward (DF) MIMO two-way relay systems, the transmission schemes are designed and the closed-form expressions for the outage probability and average symbol error rate (ASER) of the twoway relay system are derived based on two different scenarios of channel state information (CSI). For perfect CSI, the maximum-ratio-transmission and combining (MRT-MRC) technique is applied to design the beamforming and combining vectors. Without perfect CSI, the transmission scheme with limited feedback is designed, and the analytical results are verified through two kinds of codebooks, i.e., random vector quantization and Grassmann. The simulation results show that, the proposed transmission schemes for the two-way relay system can outperform other transmission schemes in the performance of outage probability and ASER, and the accuracy of the derived closed-form expressions is also verified by the numerical simulations.

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)0.002
metaresearch head score (Gemma)0.008
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: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.292
Teacher spread0.254 · 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
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

Citations0
Published2009
Admission routes1
Has abstractyes

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