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Record W1885165528 · doi:10.1109/vtc.2002.1002840

Combined space-time trellis-coded modulation and group multiuser detection

2003· article· en· W1885165528 on OpenAlexaff
T.A. Tran, A.B. Sesay

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMultiuser detectionTrellis (graph)Computer scienceTransceiverDecoding methodsSingle antenna interference cancellationAlgorithmSpace–time trellis codeComputational complexity theoryInterference (communication)Code (set theory)Convolutional codeModulation (music)Group (periodic table)Electronic engineeringWirelessReal-time computingCode division multiple accessTelecommunicationsChannel (broadcasting)Block codeConcatenated error correction codeEngineeringSet (abstract data type)Physics

Abstract

fetched live from OpenAlex

We propose a robust transceiver for space-time trellis coded modulation multiuser communications. Users in our system are divided into a number of groups using the group signatures proposed in Tran et al. (2002). The multiuser detection at the receiver is performed in group fashion after the group interference cancellation. The trellis decoding and multiuser detection are performed jointly in an iterative manner. Computer simulation results show that our receiver approaches single-user performance after three iterations. The computational complexity per user per code symbol of our receiver is much lower than the receiver proposed in Lu et al. (2000). We conclude that the proposed transceiver is robust and has low complexity for wireless multiuser communications systems.

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)0.003
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
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)0.0010.001

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 head0.007
GPT teacher head0.204
Teacher spread0.197 · 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
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

Citations1
Published2003
Admission routes1
Has abstractyes

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