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Record W2112074917 · doi:10.1109/tcsi.2005.853902

Feedforward symbol timing recovery technique using two samples per symbol

2005· article· en· W2112074917 on OpenAlex
Wei‐Ping Zhu, Yupeng Yan, M. Omair Ahmad, M.N.S. Swamy

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Circuits and Systems I Fundamental Theory and Applications · 2005
Typearticle
Languageen
FieldEngineering
TopicAdvancements in PLL and VCO Technologies
Canadian institutionsConcordia University
Fundersnot available
KeywordsAlgorithmPhase-shift keyingSymbol rateFeed forwardQuadrature (astronomy)Computer scienceKalman filterClock recoveryMathematicsElectronic engineeringBit error rateDecoding methodsTelecommunicationsEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, a new feedforward symbol timing recovery technique using timing estimation followed by interpolation is presented for digital receivers with two samples/symbol or higher sampling rate. A few timing estimation algorithms are proposed to estimate the timing offset accurately. The basic algorithm uses only the in-phase (I) or quadrature (Q) signal for timing detection, which applies to a BPSK communication system. It is shown that the basic algorithm, when applied in quadrature modulation systems where both I and Q signals are available, can be modified slightly to yield an improved estimation precision. The mean and variance of the resulting timing estimate are analyzed and simulated, supporting a satisfactory estimation performance. It is also shown that by applying a postprocessing scheme, such as the Kalman filter, the variance can be further reduced, resulting in a smoothed timing estimate. Some of the issues concerning the implementation of the proposed technique are also addressed.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.915
Threshold uncertainty score0.847

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.031
GPT teacher head0.272
Teacher spread0.241 · 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