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Record W4406890431 · doi:10.1109/access.2025.3535789

Dual-Polarization Self-Coherent Transceivers for Free Space Optical Communications in the Presence of Atmospheric Turbulence

2025· article· en· W4406890431 on OpenAlexaff
Yeganeh Nasrollahzadeh, Shiva Kumar, J Faheemunnisa bi, Mahdi Naghshvarianjahromi, M. Jamal Deen

Bibliographic record

VenueIEEE Access · 2025
Typearticle
Languageen
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsMcMaster University
FundersScience and Engineering Research Council
KeywordsTransceiverFree-space optical communicationPhysicsAtmospheric turbulencePolarization (electrochemistry)Free spaceOptical communicationTurbulenceOpticsRemote sensingTelecommunicationsComputer scienceOptoelectronicsMeteorologyGeology

Abstract

fetched live from OpenAlex

The self-coherent scheme offers significant advantages for free-space optical (FSO) communications, particularly due to its higher oscillator-signal mixing efficiency compared to conventional coherent schemes. This paper presents an analysis and simulation results of an FSO system utilizing the dual-polarization (DP) self-coherent scheme, where independent quadrature amplitude modulation (QAM) data is transmitted for each polarization. Atmospheric turbulence, however, introduces random polarization rotation and polarization-dependent phase shifts, which cause the effective channel matrix to become singular at certain angles. This leads to an increased bit error rate (BER) in conventional systems that use two photodetectors (PDs). To address this issue, we propose a novel detection scheme using three PDs. This study evaluates the performance of long-haul FSO transceivers, comparing the DP self-coherent scheme with the conventional coherent scheme under a variety of conditions, including satellite-to-ground, ground-to-satellite, and satellite-to-satellite links. The analysis spans different levels of atmospheric turbulence, ranging from weak to moderate to strong, providing a comprehensive assessment of transmission efficacy.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.648
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.000
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 head0.023
GPT teacher head0.295
Teacher spread0.272 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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