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Record W1964227892 · doi:10.1109/tvt.2013.2259643

Space–Time Trellis Codes for Two-Way Relay MIMO Channels With Single-Antenna Relay Nodes

2013· article· en· W1964227892 on OpenAlexaff
Sajjad Beygi, MohammadMehdi Kafashan, Hamid Reza Bahrami, Tho Le‐Ngoc, Mehdi Maleki

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2013
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsMcGill University
Fundersnot available
KeywordsPairwise error probabilityRelayRelay channelTrellis (graph)Upper and lower boundsSpace–time trellis codeMIMOTopology (electrical circuits)Channel state informationComputer scienceSpace–time codeDiversity gainAlgorithmChannel (broadcasting)Decoding methodsMathematicsTelecommunicationsWirelessBlock codePower (physics)Concatenated error correction codePhysicsCombinatorics

Abstract

fetched live from OpenAlex

In this paper, the analysis and design of space-time trellis codes (STTCs) for two-way relay channels (TWRCs) with multiantenna source and destination and single-antenna relay nodes, assuming perfect channel state information (CSI) at the destination nodes, is considered. We derive a pairwise error probability (PEP) expression for the performance of STTCs in this type of channels. A simple upper bound on the PEP is then derived and used to find optimum STTCs. We prove that the designed STTCs based on the derived criterion achieve full diversity and significant coding gain in TWRCs, particularly at high signal-to-noise ratios (SNRs).

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)0.005
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 score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.238
Teacher spread0.218 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

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