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Record W4246240204 · doi:10.1007/bf03391566

Low-complexity factor-graph-based MAP detector for filter bank multicarrier systems

2016· article· en· W4246240204 on OpenAlex

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

VenueJournal of Communications and Information Networks · 2016
Typearticle
Languageen
FieldEngineering
TopicPAPR reduction in OFDM
Canadian institutionsMcGill University
FundersNational Natural Science Foundation of China
KeywordsDetectorFactor graphFilter bankMinimum mean square errorComputer scienceModulation (music)WaveformElectronic engineeringOrthogonal frequency-division multiplexingMatched filterAlgorithmMultiplexingBit error rateFilter (signal processing)Control theory (sociology)MathematicsTelecommunicationsEngineeringStatisticsPhysicsDecoding methodsChannel (broadcasting)AcousticsArtificial intelligence

Abstract

fetched live from OpenAlex

FBMC (Filter Bank Multicarrier) modulation is considered one of the waveform candidates in fifth generation wireless communication technology because of its several improved features compared to conventional orthogonal frequency division multiplexing schemes. A soft-input-soft-output factor-graph-based maximum-a-posterior detector is applied to FBMC systems. The detector achieves better performance than simple linear equalizers such as minimum mean square error and zero forcing in coded systems while exhibiting only a linear growth in complexity with the number of simultaneous interfering symbols. Furthermore, the proposed detector can be easily extended to cases where FBMC modulation is combined with multiple-input-multiple-output processing. The complexity of the detector is analyzed and the simulation results demonstrated its superior performance.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.977
Threshold uncertainty score0.291

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 communication0.0000.001
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 head0.025
GPT teacher head0.244
Teacher spread0.219 · 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