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Record W2145243654 · doi:10.1109/ccece.2006.277399

A Software Defined Radio Receiver Architecture for UWB Communications and Positioning

2006· article· en· W2145243654 on OpenAlexafffund
Yanyang Zhao, Ligen Wang, Jean‐François Frigon, Chahé Nerguizian, Ke Wu, R.G. Bosisio

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave and Dielectric Measurement Techniques
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSoftware-defined radioComputer scienceRadio receiver designUltra-widebandRadio frequencyElectronic engineeringDemodulationDirect-conversion receiverSoftwareEmbedded systemComputer hardwareEngineeringTransmitterTelecommunicationsDetector

Abstract

fetched live from OpenAlex

A software defined radio (SDR) receiver architecture is proposed in this paper for ultra-wideband (UWB) applications. Both UWB data communication and positioning functions are implemented in a single hardware platform and using suitable demodulation and positioning algorithms respectively. This architecture adopts a direct conversion radio frequency (RF) front-end based on multi(six)-port technology. The features of multi(six)-port technology provide the feasibility to realize both UWB functionalities without changing the hardware configuration. Furthermore it has several advantages such as object penetration, multi-path immunity and low probability of interception. In the proposed architecture, an impulse UWB signal is considered for BPSK data communication and positioning in an indoor environment. Data communication and positioning simulation results of the considered system have been obtained for an indoor environment. A proof-of-concept experiment has been realized on a test bench that includes a multi-port RF front-end fabricated using commercial components. The measurement and simulation results validate the proposed architecture and corresponding algorithm in a 1 GHz operating band

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.623
Threshold uncertainty score0.280

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 communication0.0000.000
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 head0.014
GPT teacher head0.211
Teacher spread0.197 · 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 designBench or experimental
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
Published2006
Admission routes2
Has abstractyes

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