MétaCan
Menu
Back to cohort
Record W2120857402 · doi:10.1155/asp.2005.1736

Design Verification and Performance Evaluation of an Enhanced Wideband CDMA Receiver Using Channel Measurements

2005· article· en· W2120857402 on OpenAlexafffund
Karim Cheikhrouhou, Sofiène Affes, Ahmed Elderini, Besma Smida, P. Mermelstein, Belhassen Sultana, V. Sampath

Bibliographic record

VenueEURASIP Journal on Advances in Signal Processing · 2005
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMultipath propagationWidebandChannel (broadcasting)Computer scienceDelay spreadElectronic engineeringPower delay profileCode division multiple accessDoppler effectPower (physics)TelecommunicationsPhysicsEngineering

Abstract

fetched live from OpenAlex

The spatio-temporal array receiver (STAR) decomposes generic wideband CDMA channel responses across various parameter dimensions (e.g., time delays, multipath components, etc.) and extracts the associated time-varying parameters (i.e., analysis) before reconstructing the channel (i.e., synthesis) with increased accuracy. This work verifies the channel analysis/synthesis design of STAR by illustrating its capability to extract accurately the channel parameters (time delays and drifts, carrier frequency offsets, Doppler spread, etc.) from measured data and to adapt online to their observed time evolution in real-world propagation conditions. We also verify the performance of STAR by comparing the results achieved with generic and measured channels for an average multipath power profile of [ ] dB and a vehicular speed below 30 km/h. The results suggest that losses due to operations with real channels are only 1 dB in SNR and – % in capacity with DBPSK and single transmit and receive antennas. The corresponding SNR threshold for operation with real channels is about 5 dB.

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)0.004
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 score0.602
Threshold uncertainty score0.534

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.005
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 head0.143
GPT teacher head0.379
Teacher spread0.235 · 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
Published2005
Admission routes2
Has abstractyes

Explore more

Same venueEURASIP Journal on Advances in Signal ProcessingSame topicWireless Communication Networks ResearchFrench-language works237,207