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Record W2018631165 · doi:10.1109/dcas.2014.6965347

Signal conditioning for polar all-digital OFDM wireless transmitters

2014· article· en· W2018631165 on OpenAlexaff
Suhas Illath Veetil, Mohamed Helaoui

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSignal conditioningElectronic engineeringComputer scienceTransmitterBandwidth (computing)SIGNAL (programming language)Orthogonal frequency-division multiplexingAnalog signalElectrical engineeringDigital signal processingTelecommunicationsEngineeringChannel (broadcasting)PhysicsPower (physics)

Abstract

fetched live from OpenAlex

A novel scheme for conditioning OFDM modulated signals is proposed to facilitate the implementation of mixerless all-digital transmitter using polar RF Digital to Analog Converters (RFDACs). The signal conditioning is applied to the OFDM signal to avoid phase discontinuities, thus mitigating the effects of bandwidth expansion in polar decomposition. The absence of mixers and filters offers wide RF bandwidth and takes the design a step closer to reconfigurable transmitter. To validate this concept, the signal conditioning is applied to an LTE signal and sent to a phase modulator circuit. The performance of the phase modulator in terms of signal quality is assessed using Normalized Mean Square Error (NMSE) metric. It is concluded that the signal conditioning results in a signal that may not comply with the existing standards, but is spectrally efficient and thus enables the use of polar architectures for wideband signals.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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 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

Citations1
Published2014
Admission routes1
Has abstractyes

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