MétaCan
Menu
Back to cohort
Record W1834824654 · doi:10.1109/ccece.2001.933669

A transmit-diversity coding framework for cellular systems

2002· article· en· W1834824654 on OpenAlexaff
Marianne Godbout, H. Leib

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsTransmit diversityBlock codeConvolutional codeComputer scienceFadingAntenna diversityRayleigh fadingCoding gainDiversity gainTelecommunicationsAlgorithmElectronic engineeringCoding (social sciences)Diversity combiningAntenna (radio)Decoding methodsMathematicsEngineeringStatistics

Abstract

fetched live from OpenAlex

Space-time (ST) coding is an efficient technique for combatting short-term fading in telecommunication systems with multiple transmit antennas. This paper presents a multi-dimensional framework for such a system that contrasts two approaches for assigning signal dimensions to antennas. With aggregate transmit antennas (ATA), a dimension is employed by all the antennas while in orthogonal transmit antennas (OTA), each dimension is employed by a single transmit antenna. We provide a diversity order analysis for repetition codes and Tarokh-Seshadri-Calderbank (see IEEE Transactions on Information Theory, vol.44, p.744-64, 1998) codes within our framework over a spatially uncorrelated, block-fading Rayleigh channel. Simulation results for repetition, TSC, and classical convolutional codes using both OTA and ATA transmission are presented. With ATA, classical convolutional codes perform comparably to the known TSC codes, while with OTA diversity gain is easier to obtain than with ATA. All the codes that were considered in this work with OTA show performance gains with respect to ATA.

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: Methods · Consensus signal: none
Teacher disagreement score0.876
Threshold uncertainty score0.341

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 science0.0000.000
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 head0.040
GPT teacher head0.233
Teacher spread0.192 · 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
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

Citations6
Published2002
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced Wireless Communication TechniquesFrench-language works237,207