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Record W1914462256 · doi:10.1109/isssta.1996.563183

Non-orthogonal CDMA forward link offers flexibility without compromising capacity

2002· article· en· W1914462256 on OpenAlexaff
J.H.-M. Sau, E.S. Sousa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceOrthogonalityFadingRayleigh fadingSpectral efficiencyCode division multiple accessChannel capacityCDMA spectral efficiencyRician fadingAlgorithmChannel (broadcasting)Electronic engineeringComputer networkDecoding methodsMathematicsEngineering

Abstract

fetched live from OpenAlex

We proposed the use of non-orthogonal CDMA forward link in future micro-cellular multi-media CDMA systems. It allows the use of mobile-transparent hand-off, a hand-off method which does not require sending a control signal to a mobile for its initiation. This reduces the hand-off related signaling overhead, therefore makes non-orthogonal forward links well suited to micro-cellular systems where there will be frequent hand-offs. Comparing with orthogonal forward links, it also allow much more flexibility in data rates and channel coding rates, hence readily accommodate high bit rate or VBR services. In addition, the possibility of using low-rate error control codes in non-orthogonal links provides a way to compensate for the loss in capacity due to non-orthogonality. Simulation results show that when the flexibility in error control coding is exploited, orthogonal and non-orthogonal forward links have comparable capacities in Rayleigh fading channels. In static channels (or very slowly fading Rician channels), orthogonal forward links have higher capacities, but the capacities of non-orthogonal forward links are not much worse than those of orthogonal forward links when the path loss exponent is small.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.965
Threshold uncertainty score0.710

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 communication0.0000.001
Open science0.0020.001
Research integrity0.0000.001
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 head0.086
GPT teacher head0.298
Teacher spread0.212 · 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
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

Citations4
Published2002
Admission routes1
Has abstractyes

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