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Record W1758890658 · doi:10.1109/vetecs.2004.1390552

Performance of an asynchronous CDMA system with parallel interference cancellation

2005· article· en· W1758890658 on OpenAlexaff
M. Ghotbi, M. Reza Soleymani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsConcordia University
Fundersnot available
KeywordsSingle antenna interference cancellationAsynchronous communicationAdditive white Gaussian noiseCovariance matrixSynchronous CDMACode division multiple accessInterference (communication)Computer scienceAlgorithmMathematicsEigenvalues and eigenvectorsWhite noiseControl theory (sociology)Channel (broadcasting)Multiuser detectionTelecommunicationsDecoding methodsPhysics

Abstract

fetched live from OpenAlex

The asymptotic performance of a multistage linear partial parallel interference cancellation (PPIC) receiver for an asynchronous code division multiple access (CDMA) system over an additive white Gaussian noise (AWGN) channel using the moments of the eigenvalues of the correlation matrix is addressed. The closed-form expression for the eigenvalues of a random covariance matrix in synchronous case is known (Jonsson, D., 1982). However, for the case where the users are not synchronous, the distribution is not known. In such a case, we evaluate the behavior of the eigenvalues through numerical simulation. The figure of merit to evaluate the performance is the signal-to-interference-plus-noise ratio (SINR) that is calculated under large-system conditions where the number of active users (K) and the processing gain (N) tend to infinity, while their ratio (/spl beta/=K/N) is a finite value. Simulation results show a significant degradation in performance due to asynchrony. It is also seen that, similar to the synchronous case, the SINR for the asynchronous case in a large-system scenario converges to that of the minimum mean-squared error (MMSE) 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)0.000
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: Empirical · Consensus signal: none
Teacher disagreement score0.539
Threshold uncertainty score0.269

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 communication0.0000.001
Open science0.0010.000
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 head0.019
GPT teacher head0.257
Teacher spread0.238 · 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

Citations5
Published2005
Admission routes1
Has abstractyes

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