MétaCan
Menu
Back to cohort
Record W2154118207 · doi:10.1109/twc.2009.080229

Multiuser decorrelator detectors in MIMO CDMA systems over Nakagami fading channels

2009· article· en· W2154118207 on OpenAlexaff
Ariel Lionel Sacramento, Walaa Hamouda

Bibliographic record

VenueIEEE Transactions on Wireless Communications · 2009
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsConcordia University
Fundersnot available
KeywordsFadingNakagami distributionComputer scienceMultiuser detectionMIMOCode division multiple accessPhase-shift keyingBit error rateTelecommunications linkElectronic engineeringFading distributionDiversity schemeAlgorithmTelecommunicationsRayleigh fadingDecoding methodsChannel (broadcasting)Engineering

Abstract

fetched live from OpenAlex

Space-time spreading has been employed to exploit the spatial diversity in multiple-input multiple-output (MIMO) code-division multiple-access (DS-CDMA) systems. In the presence of multiuser interference, resulting from the cross-correlation between users' code sequences, the full system diversity cannot be achieved when using the conventional matched receiver. In this paper we investigate the performance of space-time spreading (STS) and transmit diversity in the uplink of a MIMO DS-CDMA system over Nakagami-m fast-fading channels. The space-time system employs N = 2 transmit antennas and L receive antennas at the user side and base-station, respectively. We analyze the performance of the system when a linear decorrelator detector is employed to mitigate the effect of multiuser interference. In our analysis, we start by finding the probability density function (pdf) of the signal-to-interference ratio (SINR) at the output of the space-time combiner. Using this pdf, we derive a closed form expression for the bit-error rate (BER) for binary phase-shift keying (BPSK) transmission. The accuracy of the derived pdf and BER expression are verified using simulations, for different values of fading figure m and number of users.

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 categoriesMeta-epidemiology (narrow)
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 score0.795
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.021
GPT teacher head0.270
Teacher spread0.249 · 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.

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

Citations7
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Wireless CommunicationsSame topicAdvanced Wireless Communication TechniquesFrench-language works237,207