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Record W2130890929 · doi:10.1109/cjece.2006.259202

Ultra-wideband rake receiver based on single-bit processing

2006· article· en· W2130890929 on OpenAlexafffundvenue
John Nielsen, R. Pasand

Bibliographic record

VenueCanadian Journal of Electrical and Computer Engineering · 2006
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRake receiverBit (key)Computer scienceWidebandRakeElectronic engineeringTelecommunicationsEngineeringComputer networkFadingChannel (broadcasting)

Abstract

fetched live from OpenAlex

Efficient detection of the ultra-wideband(UWB) pulses used in pulse-position-modulationcommunicationlinks is a challenge due to the combinationof a significant level of multipath spreading encounteredin indoor wireless channels and the wide bandwidth of the transmitted pulses. If a conventionalrake receiver is used, then a large number of fingers are requiredto capture the pulse energy; this represents a formidable implementationchallenge due to the intensive signal processingrequired. To mitigate the problem, a rakereceiver based on uniformsampling and single-bitsampling quantizationis proposed. This approach significantly reduces the complexity and processing requirements, so that a large number of rake fingers can be efficiently implemented. The architecture and processing of the proposed receiver, denoted as the single-bit rake (SBR), are described in this paper. A comparative performance analysis of the SBR relative to a conventional rake is also given. As shown in this paper, if the transmitted ultra-wideband pulses are resolvable after passing through the multipath propagation channel, then the penalty of using single-bit quantization is limited to dB compared to a conventional rake receiver with no quantization distortion. For typical indoor wireless propagation channels it is shown that this penalty can typically be avoided by using more rake fingers. However, this incremental processing is relatively insignificant, as uniform single-bit quantization is used. The paper presents further analysis for cases in which the multipath delay spreading is so large that successive UWB pulses significantly overlap. A comparison of the SBR and conventionalrakes in terms of power consumptionand complexityis also given, demonstratingthe advantage of the SBR architecture.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.771
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.005
GPT teacher head0.152
Teacher spread0.148 · 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

Citations2
Published2006
Admission routes3
Has abstractyes

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