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Record W2121656009 · doi:10.1109/vetecs.2009.5073477

Cramer-Rao Bounds for SNR Estimates in Multicarrier Transmissions

2009· article· en· W2121656009 on OpenAlexaff
Faouzi Bellili, Alex Stéphenne, Sofiène Affes

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsEricsson (Canada)Institut National de la Recherche Scientifique
Fundersnot available
KeywordsSubcarrierAdditive white Gaussian noiseOrthogonal frequency-division multiplexingEstimatorSignal-to-noise ratio (imaging)Cramér–Rao boundAlgorithmChannel (broadcasting)Computer scienceMultiplexingUpper and lower boundsMathematicsStatisticsTelecommunicationsMathematical analysis

Abstract

fetched live from OpenAlex

Considering orthogonal frequency division multiplexing (OFDM) transmissions, we derive analytical expressions for the Cramer-Rao bounds for the subcarrier signal-to-noise ratio (SNR) estimates. The channel coefficients of the different subcarriers are assumed to be constant over the observation interval and the received signal is assumed to be corrupted by additive white Gaussian noise (AWGN). We will show that exploiting the mutual information between the different tones improves the achievable performance of subcarrier SNR estimators.

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)0.009
metaresearch head score (Gemma)0.056
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.056
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.003
Science and technology studies0.0010.004
Scholarly communication0.0040.005
Open science0.0030.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.002

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 head0.012
GPT teacher head0.287
Teacher spread0.276 · 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 designTheoretical or conceptual
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

Citations5
Published2009
Admission routes1
Has abstractyes

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