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Record W1484098127 · doi:10.1109/wirles.2005.1549454

Space-Time Spreading for MIMO CDMA-Based Systems over Fast-Fading Channels

2005· article· en· W1484098127 on OpenAlexafffund
Walaa Hamouda

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFadingComputer scienceDiversity schemeTransmit diversityCode division multiple accessMIMOSingle antenna interference cancellationAntenna diversityDiversity gainElectronic engineeringInterference (communication)Time diversityChannel (broadcasting)TelecommunicationsAlgorithmAntenna (radio)Engineering

Abstract

fetched live from OpenAlex

In this paper, a transmit diversity scheme based on space-time spreading for code-division multiple-access (CDMA) systems in fast-fading channels is introduced. The proposed transmit diversity scheme employs orthogonal Walsh spreading codes to exploit the time diversity introduced by the channel, and hence achieves two-fold of the diversity order obtained using conventional space-time spreading schemes. A generalization of the proposed coding scheme to systems with different antenna configurations is also discussed. For slowly-fading channels, we show that the proposed transmit diversity scheme reduces to that of conventional transmit diversity schemes with no performance degradation. For nonorthogonal spreading codes, we propose a receiver structure that employs an interference cancellation scheme to compensate for signal interference. Our results show a large performance improvement when using such an interference cancellation scheme relative to the conventional matched filter receiver.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.916
Threshold uncertainty score0.680

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
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 head0.032
GPT teacher head0.296
Teacher spread0.264 · 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

Citations7
Published2005
Admission routes2
Has abstractyes

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