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Record W2148342136 · doi:10.1109/tsp.2011.2128314

Closed-Form Expressions for the Exact Cramér–Rao Bounds of Timing Recovery Estimators From BPSK, MSK and Square-QAM Transmissions

2011· article· en· W2148342136 on OpenAlexaff
Ahmed Masmoudi, Faouzi Bellili, Sofiène Affes, Alex Stéphenne

Bibliographic record

VenueIEEE Transactions on Signal Processing · 2011
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsPhase-shift keyingMathematicsAdditive white Gaussian noiseCramér–Rao boundMinimum-shift keyingEstimatorQuadrature amplitude modulationQAMAlgorithmNyquist–Shannon sampling theoremWhite noiseAmplitude and phase-shift keyingUpper and lower boundsStatisticsDecoding methodsBit error rateMathematical analysis

Abstract

fetched live from OpenAlex

In this paper, we derive for the first time analytical expressions for the exact Cramér–Rao lower bounds (CRLB) for symbol timing recovery of binary phase shift keying (BPSK), minimum shift keying (MSK), and square QAM-modulated signals. It is assumed that the transmitted data are completely unknown at the receiver and that the shaping pulse verifies the first Nyquist criterion. Moreover the carrier phase and frequency are considered as unknown nuisance parameters. The time delay remains constant over the observation interval and the received signal is corrupted by additive white Gaussian noise (AWGN). Our new expressions prove that the achievable performance holds irrespective of the true time delay value. Moreover, they corroborate previous attempts to empirically compute the considered bounds thereby enabling their immediate evaluation.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.906
Threshold uncertainty score0.834

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 studies0.0010.000
Scholarly communication0.0000.001
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 head0.044
GPT teacher head0.271
Teacher spread0.227 · 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 designOther design
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

Citations25
Published2011
Admission routes1
Has abstractyes

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