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Record W1540928614 · doi:10.1109/pimrc.2005.1651621

Differential Space-Time Modulation for Transmissions over Unknown FIR Channels

2006· article· en· W1540928614 on OpenAlexaff
Zhan Zhang, Jacek Ilow

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMIMOTransceiverComputer scienceMultipath propagationFadingFinite impulse responseDemodulationAlgorithmElectronic engineeringBlock codeToeplitz matrixChannel (broadcasting)Block (permutation group theory)Control theory (sociology)TelecommunicationsMathematicsDecoding methodsEngineeringWireless

Abstract

fetched live from OpenAlex

This paper presents a differential space-time transceiver over multiple-input multiple-output (MIMO) time-dispersive channels. The underlying idea is to transform the block Toeplitz received signal structure caused by the finite impulse response (FIR) multipath channels into a block circulant matrix structure by deploying specialized signaling when transmitting through multiple antennas. This structure is then exploited by using the efficient space-time (ST) processing at the receiver for the mitigation of the channel multipath effects. The new, low complexity, transceiver is based on cyclic group codes and a differential ST demodulation. The proposed scheme falls into the category of the deterministic approaches that are able to operate on short sample data and do not face convergence problems. Simulation results verify the robust performance of the scheme over the MIMO-FIR fading channels.

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)0.000
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.008
GPT teacher head0.230
Teacher spread0.223 · 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
Published2006
Admission routes1
Has abstractyes

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