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Record W2367195929

The Error Analysis of STTC for MB-OFDM UWB Communications

2010· article· en· W2367195929 on OpenAlexaff
Li Yu

Bibliographic record

VenueDianzi xuebao · 2010
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsFadingOrthogonal frequency-division multiplexingSpatial correlationAntenna diversityComputer scienceRayleigh fadingSpace–time trellis codeViterbi decoderTrellis (graph)TransmitterDiversity gainElectronic engineeringAlgorithmSpatial multiplexingChannel (broadcasting)Decoding methodsAntenna (radio)TelecommunicationsMIMOBlock codeEngineeringError floor
DOInot available

Abstract

fetched live from OpenAlex

Space-time trellis codes(STTC) in multiband orthogonal frequency division multiplexing(MB-OFDM) systems with Rayleigh fading channels is proposed and diversity performance with spatial correlation between the transmit antennas is analyzed.STTC converts the single output code symbol into several code symbols,which are to be transmitted simultaneously from multiple transmitter antennas.Viterbi optimal soft decision decoding algorithm is used at the receiver.We investigate both quasi-static and interleaved channels and demonstrate how the spatial fading correlation affects the performance over these two different MB-OFDM wireless channel models.Diversity order is preserved when the spatial correlation is small.While spatial correlation becomes larger,diversity order could maintain in interleaved channels but decrease in quasi-static channels.In general,the performance of space time codes is observed to be robust to spatial correlation.

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)0.009
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
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)0.0010.000

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 head0.020
GPT teacher head0.312
Teacher spread0.292 · 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
Published2010
Admission routes1
Has abstractyes

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