K-Means Clustering Empowered Accuracy Improvement of Longitudinal Power Profile Estimation

We report a K-means clustering empowered longitudinal power profile estimation (PPE) scheme, achieving a 0.1 dB power estimation accuracy improvement of anomalous loss, in comparison with traditional training symbols enabled PPE.

Authors

Institutions

Publication Details

Published
2025-06-29
DOI
https://doi.org/10.23919/oecc/psc62146.2025.11110843
Primary Topic
Engineering Applied Research
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

K-Means Clustering Empowered Accuracy Improvement of Longitudinal Power Profile Estimation

Junjiang Xiang, Gai Zhou, Songnian Fu, Meng Xiang et al.
Engineering Applied Research
article

K-Means Clustering Empowered Accuracy Improvement of Longitudinal Power Profile Estimation

Junjiang Xiang, Gai Zhou, Songnian Fu, Meng Xiang, Yuwen Qin
article en

Abstract

We report a K-means clustering empowered longitudinal power profile estimation (PPE) scheme, achieving a 0.1 dB power estimation accuracy improvement of anomalous loss, in comparison with traditional training symbols enabled PPE.

Photonic Science (United Kingdom) (GB)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 78%
Engineering Applied Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.