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Investigating Power Multiplexing for Coherent Microwave Photonic Links

2024· article· en· W4404036603 on OpenAlexaff
Amir Abbas Sardarzadeh, Peng Li, Jianping Yao

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Photonic Communication Systems
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMultiplexingPhotonicsMicrowaveComputer scienceElectronic engineeringPower (physics)Electrical engineeringOptoelectronicsTelecommunicationsPhysicsEngineering

Abstract

fetched live from OpenAlex

We propose a new technique to use power multiplexing in a coherent radio-over-fiber (RoF) link to increase the data rate without using additional optical wavelengths. At the transmitter, a continuous-wave (CW) light wave from a laser diode (LD) is modulated by two pairs of power-multiplexed microwave vector signals using a dual-drive Mach-Zehnder modulator (DD-MZM). The optical signal at the output of the DD-MZM is transmitted over a single-mode fiber (SMF) to a coherent receiver, where coherent detection is performed. A digital signal processing (DSP) algorithm is developed to mitigate the phase noise and unstable offset frequency between the transmitter and local oscillator light sources and to de-multiplex the power-multiplexed signals. An experiment is performed. For the transmission of four power-multiplexed vector signals over 10 km of SMF with two 16QAM lower-power signals and two QPSK higher-power signals, all at a carrier frequency of 4 GHz and a baud rate of 0.5 GSym/s, 3.7% error vector magnitudes (EVMs) for the 16QAM signals and 2.5% for the QPSK signals are achieved for a received optical power of -1 dBm. Error-free transmission is achieved.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.837
Threshold uncertainty score0.569

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 science0.0000.000
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 head0.027
GPT teacher head0.278
Teacher spread0.251 · 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 designBench or experimental
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
Published2024
Admission routes1
Has abstractyes

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