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Record W2153291654 · doi:10.1109/icc.2005.1494788

Multicarrier-CDMA STAR with time and frequency synchronization

2005· article· en· W2153291654 on OpenAlexaff
Besma Smida, Sofiène Affes, Jun Li, P. Mermelstein

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsSubcarrierCarrier frequency offsetComputer scienceCode division multiple accessMultipath propagationElectronic engineeringBandwidth (computing)Spread spectrumOrthogonal frequency-division multiplexingChannel (broadcasting)Frequency offsetComputer networkEngineering

Abstract

fetched live from OpenAlex

This paper proposes a spectrum-efficient spatio-temporal array-receiver (STAR) for multi-carrier CDMA systems named MC-STAR. First, we derive a new post-correlation model for MC-CDMA that supports both the MT-CDMA and MC-DS-CDMA air-interfaces. Based on this model, we introduce a new multi-carrier receiver with rapid and accurate joint synchronization in time and frequency. We also exploit the intrinsic subcarrier correlation to improve the channel identification and the synchronization operations. We analyze the performance of MC-STAR in an unknown time-varying Rayleigh channel with multipath, carrier offset and cross-correlation between subcarrier channels. Simulation results confirm the accuracy of the joint time/frequency synchronization. They also confirm that for each MC-STAR configuration there exists an optimum number of subcarriers which results in maximum throughput. A higher number of subcarriers increases the inter-carrier interference while a lower number of subcarriers reduces the frequency gain. With four receiving antennas and five MT-CDMA subcarriers in 5 MHz bandwidth, MC-STAR provides about 1.2 bps/Hz at low mobility for DBPSK, i.e., an increase of 30% in spectrum efficiency over DS-CDMA.

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: Methods · Consensus signal: none
Teacher disagreement score0.986
Threshold uncertainty score0.236

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 head0.010
GPT teacher head0.243
Teacher spread0.233 · 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

Citations5
Published2005
Admission routes1
Has abstractyes

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