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Record W4386477993 · doi:10.1364/oe.493545

Layered hybrid PAM-DMT for IM/DD OWC systems

2023· article· en· W4386477993 on OpenAlexaff
Zuhang Geng, Xinke Tang, Xiaoping Zhang, Yuhan Dong

Bibliographic record

VenueOptics Express · 2023
Typearticle
Languageen
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsToronto Metropolitan University
FundersBasic and Applied Basic Research Foundation of Guangdong ProvinceNatural Science Foundation of Shenzhen City
KeywordsOrthogonal frequency-division multiplexingModulation (music)OrthogonalitySpectral efficiencyComputer scienceOpticsIntensity modulationPulse-amplitude modulationOptical wireless communicationsPower (physics)Optical wirelessElectronic engineeringWirelessPhysicsTelecommunicationsPulse (music)Phase modulationChannel (broadcasting)AcousticsMathematicsPhase noiseEngineering

Abstract

fetched live from OpenAlex

Traditional pulse-amplitude-modulated discrete multitone modulation (PAM-DMT) suffers from poor overall performance of spectral and power efficiencies in optical wireless communication (OWC) systems. We propose layered hybrid PAM-DMT (LHPAM-DMT) to utilize more subcarriers to improve the performance. The real part of frequency domain signal is divided into several layers and symmetry biases are added in time domain to generate real-valued and nonnegative signals for intensity modulation with direct detection (IM/DD) OWC systems. By appropriately designing the orthogonality between the signals in lower layers and signals & added biases in higher layers, we further propose an iterative receiver to recover the transmitted information. Theoretical derivation proves that LHPAM-DMT has higher spectral efficiency than PAM-DMT and lower complexity than layered PAM-DMT. Numerical results suggest that LHPAM-DMT is more power efficient than PAM-DMT as well as direct-current (DC) biased optical OFDM (DCO-OFDM), one of the most popular schemes.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.369
Threshold uncertainty score0.643

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 head0.028
GPT teacher head0.251
Teacher spread0.222 · 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 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

Citations4
Published2023
Admission routes1
Has abstractyes

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