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Record W2585233219

Embedded pilot and multi-size OFDM processing for jointly time and frequency selective channels

2016· article· en· W2585233219 on OpenAlexaff
Christian Schlegel, Marcel Jar

Bibliographic record

VenueInternational Symposium on Information Theory and its Applications · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPilot signalOrthogonal frequency-division multiplexingComputer scienceFrequency domainImpulse responseTime domainChannel (broadcasting)SIGNAL (programming language)Frequency dividerElectronic engineeringTime–frequency analysisSignal processingMIMOImpulse (physics)TelecommunicationsEngineeringMathematicsRadar
DOInot available

Abstract

fetched live from OpenAlex

An embedded pilot signal structure is presented that allows simultaneous processing for time-, and frequency-selective channel variations. The pilot signal is added to an OFDM data signal in the time domain and allows for variable power allocation to the pilot. The structure of the signal shows up as a tone comb in the frequency domain which allows for doppler tracking. It is shown that exact tone interpolation for the data carriers can be achieved as long as the channel's impulse response does not exceed the duration of a period of the pilot signal. Finally, pilots can be allocated in a frequency-division mode to multiple transmitters for MIMO or joint detection applications, avoiding the problem of pilot contamination.

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)0.001
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.262
Teacher spread0.251 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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