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Record W7028315897

Estimation of sparse channels in IR-UWB systems

2014· dissertation· en· W7028315897 on OpenAlexaff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2014
Typedissertation
Languageen
FieldEngineering
TopicSparse and Compressive Sensing Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsCompressed sensingUltra-widebandNyquist rateWirelessChannel (broadcasting)Bandwidth (computing)Multipath propagationPulse-position modulationFilter (signal processing)Orthogonal frequency-division multiplexing
DOInot available

Abstract

fetched live from OpenAlex

Ultra Wideband (UWB) is a rapidly growing technology for digital wireless communications.UWB utilizes low power, ultra short, pulses and is specifically suited for short-range, high-rate indoor wireless communications as well as fine localization applications.The attractive properties of UWB are a direct consequence of its very wide bandwidth that also implies an extremely high Nyquist sampling rate, so digital processing of UWB signals requires the use of fast and efficient, and therefore, expensive, Analog to Digital Converters.UWB systems are commonly used in indoor environments in which propagation is characterized by long but sparse multipath channels.Multi-path channels cause intersymbol interference (ISI) and introduce distortion to the received signal.To counter the effects of the channel, the channel impulse response must be accurately estimated at the receiver.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.016
GPT teacher head0.231
Teacher spread0.215 · 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.

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

Citations0
Published2014
Admission routes1
Has abstractyes

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