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Record W2164441749 · doi:10.1109/ccece.2006.277761

MAP Frame Synchronization for PSAM Systems

2006· article· en· W2164441749 on OpenAlexafffund
Haozhang Jia, D.E. Dodds

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Saskatchewan
KeywordsComputer scienceFadingSynchronization (alternating current)AlgorithmAdditive white Gaussian noiseFrame (networking)Symbol (formal)Modulation (music)WirelessReal-time computingChannel (broadcasting)TelecommunicationsDecoding methods

Abstract

fetched live from OpenAlex

Pilot symbol assisted modulation (PSAM) is a method to compensate for fading in wireless mobile communications. Known pilot symbols are periodically inserted into the data symbol stream and the receiver uses these symbols to derive the amplitude and phase reference required to set decision thresholds. In mobile communication, PSAM can facilitate the use of more bandwidth efficient modulation schemes. This paper focuses on frame synchronization to locate the time position of pilot symbols. We investigate a MAP estimation algorithm that assumes high SNR but also works well in moderate SNR. It retains good performance over a wide range of SNR in the AWGN and fading channels and frequency offsets as high as 10% of the symbol rate. The computed mean time to correct symbol timing decreases with increasing SNR and essentially reaches a minimum for any SNR greater than 6 dB. Also, the computational complexity of this MAP algorithm is almost as simple as the ML algorithm

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)0.002
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.219
Teacher spread0.213 · 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

Citations0
Published2006
Admission routes2
Has abstractyes

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