MétaCan
Menu
Back to cohort
Record W2962263571 · doi:10.1109/icc.2019.8761387

Absolute Value Layered ACO-OFDM for Intensity-Modulated Optical Wireless Channels

2019· article· en· W2962263571 on OpenAlex
Ruowen Bai, Steve Hranilovic

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsOrthogonal frequency-division multiplexingBit error rateSpectral efficiencyElectronic engineeringOptical wirelessModulation (music)Computer scienceWirelessChannel (broadcasting)OpticsPhysicsTelecommunicationsEngineeringAcoustics

Abstract

fetched live from OpenAlex

Both enhanced unipolar orthogonal frequency division multiplexing (eU-OFDM) and layered asymmetrically clipped optical OFDM (LACO-OFDM) are among the most spectrally efficient modulation techniques for intensity modulated links that layer multiple non-negative signals. In this paper, we propose absolute value layered asymmetrically clipped optical OFDM (ALACO-OFDM) which further improves the spectral efficiency while using a smaller number of layers and no direct current (DC) bias. In ALACO-OFDM, asymmetrically clipped optical OFDM (ACO-OFDM) signals are sent at the first L layers and absolute value optical OFDM (AVO-OFDM) is used for the remaining subcarriers and transmitted simultaneously. Analysis indicates that ALACO-OFDM achieves higher spectral efficiency and smaller peak-to-average power ratio (PAPR) compared to eU-OFDM, LACO-OFDM and asymmetrically clipped absolute value optical OFDM (AAO-OFDM). Monte Carlo simulation results also indicate that ALACO-OFDM achieves significant bit error rate (BER) performance gains compared to its counterparts at the same spectral efficiency.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.734
Threshold uncertainty score0.773

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 science0.0010.000
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 head0.013
GPT teacher head0.227
Teacher spread0.214 · 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