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Record W2156842468 · doi:10.1109/spawc.2005.1506062

A multicarrier-CDMA receiver with full interference suppression and carrier frequency offset recovery

2005· article· en· W2156842468 on OpenAlexaff
Besma Smida, Sofiène Affes, K. Jamaoui, P. Mermelstein

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsCarrier frequency offsetSubcarrierElectronic engineeringComputer scienceMultipath propagationInterference (communication)Frequency offsetCode division multiple accessSingle antenna interference cancellationAdjacent-channel interferenceOrthogonal frequency-division multiplexingMultiuser detectionOffset (computer science)Co-channel interferenceMultipath interferenceIntersymbol interferenceChannel (broadcasting)TelecommunicationsEngineering

Abstract

fetched live from OpenAlex

A low-complexity multicarrier-CDMA space-time receiver with full interference suppression capabilities is developed and analyzed for high-rate transmissions over next-generation CDMA systems. First, we derive a complete model of the interference which takes into account MAI, ISI, and ICI. Based on this model, we introduce a new multicarrier interference subspace rejection (MC-ISR) receiver and analyze its performance in an unknown time-varying Rayleigh channel with multipath, carrier offset and cross-correlation between subcarrier channels. We also propose a realistic implementation of this receiver which includes an efficient strategy for carrier offset recovery in a multicarrier and multiuser detection scheme. Simulation results confirm the net advantage of the full interference suppression capabilities of MC-ISR.

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.018
GPT teacher head0.262
Teacher spread0.245 · 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

Citations5
Published2005
Admission routes1
Has abstractyes

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