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Record W2096191759 · doi:10.1109/tvt.2005.844664

Design and VLSI Implementation for a WCDMA Multipath Searcher

2005· article· en· W2096191759 on OpenAlexaff
Eugene Grayver, Jean‐François Frigon, Ahmed M. Eltawil, A. Tarighat, K. Shoarinejad, Aliazam Abbasfar, Danijela Čabrić, Babak Daneshrad

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2005
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsRake receiverMultipath propagationComputer scienceElectronic engineeringInterleavingFadingWidebandCMOSDelay spreadCode division multiple accessSpread spectrumChannel (broadcasting)EngineeringTelecommunications

Abstract

fetched live from OpenAlex

The third generation (3G) of cellular communications standards is based on wideband CDMA. The wideband signal experiences frequency selective fading due to multipath propagation. To mitigate this effect, a RAKE receiver is typically used to coherently combine the signal energy received on different multipaths. An effective multipath searcher is, therefore, required to identify the delayed versions of the transmitted signal with low probability of false alarm and misdetection. This paper presents an efficient and novel WCDMA multipath searcher design and VLSI architecture that provides a good compromise between complexity, performance, and power consumption. Novel multipath searcher algorithms such as time domain interleaving and peak detection are also presented. The proposed searcher was implemented in 0.18 /spl mu/m CMOS technology and requires only 150 k gates for a total area of 1.5 mm/sup 2/ consuming 6.6 mw at 100 MHz. The functionality and performance of the searcher was verified under realistic conditions using a channel emulator.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.040
GPT teacher head0.333
Teacher spread0.293 · 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 designBench or experimental
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

Citations21
Published2005
Admission routes1
Has abstractyes

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