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Record W4416178012 · doi:10.1109/jlt.2025.3632676

Toward Acceleration and Simplification of Coherent DSP Chains: Frequency Pilot Sub-Carrier Strategies for Coherent Systems

2025· article· W4416178012 on OpenAlexaff
Ahmed Medra, Mohammed Y. S. Sowailem, Chuandong Li, Xingyu Zhou

Bibliographic record

VenueJournal of Lightwave Technology · 2025
Typearticle
Language
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsHuawei Technologies (Canada)
Fundersnot available
KeywordsInitializationOverhead (engineering)Payload (computing)Frame (networking)Digital signal processingMultiplexingPilot signalSoftware portabilityTransceiver

Abstract

fetched live from OpenAlex

This paper focuses on the development of narrow-bandwidth pilot sub-carriers designed to reduce signaling overhead and computational complexity while enabling fast and robust initialization of coherent optical systems. A proper DSP implementation for the transceiver design, along with a specific frame structure, is proposed. Fast initialization algorithms are introduced, including local-oscillator-frequency-offset (LOFO) estimation, coarse chromatic dispersion (CD) estimation, and clock recovery, utilizing two pilot sub-carriers and specific training sequence (TS) design. These algorithms are designed to be fast, robust, and capable of significantly reducing receiver response time—a critical feature for dynamic and low-latency optical networks. Moreover, the application of pilot sub-carriers is explored as a means to reduce implementation complexity by eliminating the need for complex modules (e.g., clock recovery and carrier recovery (CR)), while maintaining functionality through pilot sub-carriers. Additionally, frequency and phase tracking techniques using pilot sub-carriers are proposed. This approach removes the overhead associated with pilot symbols from the payload frame structure, resulting in performance improvements, cost reduction, and enhanced 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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.031
GPT teacher head0.269
Teacher spread0.238 · 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.

Study designTheoretical or conceptual
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
Published2025
Admission routes1
Has abstractyes

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