Leistungs- und Fehleranalyse von Kommunikationsnetzen unter Verwendung von Lernmodellen mit Modellinterpretation

Details

Veröffentlichungs-Nr.

EP4120653

Aktenzeichen

EP22185033

Anmeldetag

14. Juli 2022

Veröffentlichung

2. April 2025

Erteilung

2. April 2025

Rechtsraum

EP

IPC

H04L41/14H04L41/147H04L41/0631// H04L43/08H04L41/142H04L41/16H04L41/069H04L43/022H04L43/20H04L41/122H04L43/045

Abstract

In an embodiment computer-readable training data records characterizing operation of a communication network may be used to train a machine learning (ML) model of network performance to predict expected performance characteristics, each training data record including operational features and observed performance characteristics of the communication network. The ML model may compute mappings of feature-values pairs to predicted performance characteristics. For a first subset of training data records, a fair distribution of first quantitative contributions of operational features of predicted performance characteristics may be computed, the first subset representing a baseline of observed performance characteristics. For a second subset of training data records, a fair distribution of second respective quantitative contributions of predicted performance characteristics may be computed, the second subset representing a problematic performance characteristic. Comparing the first and second quantitative contributions may determine a degradation metric for associating operational features of the second subset with the problematic performance characteristic.

Anmelder

Firmen

EXFO Solutions SAS
EXFO Inc.

Land

🇫🇷 Frankreich

Fachgebiete