From Fragmented OSS to Self-Healing Networks: Digital Twin Architecture Patterns for Autonomous Telecommunications Operations

Argues that telecom pursuit of self-healing networks is blocked by ontological fragmentation of OSS/BSS silos. Proposes a three-layer digital twin architecture — data harmonization, physics-informed simulation, and agentic AI squads — that makes legacy infrastructure legible to AI.

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-14
DOI
https://doi.org/10.5281/zenodo.20181480
Primary Topic
Digital Transformation in Industry
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

From Fragmented OSS to Self-Healing Networks: Digital Twin Architecture Patterns for Autonomous Telecommunications Operations

Nicolas Waern
Zenodo (CERN European Organization for Nuclear Research)
Digital Transformation in Industry
article

From Fragmented OSS to Self-Healing Networks: Digital Twin Architecture Patterns for Autonomous Telecommunications Operations

Nicolas Waern
article en

Abstract

Argues that telecom pursuit of self-healing networks is blocked by ontological fragmentation of OSS/BSS silos. Proposes a three-layer digital twin architecture — data harmonization, physics-informed simulation, and agentic AI squads — that makes legacy infrastructure legible to AI.

Zenodo (CERN European Organization for Nuclear Research)
Association of British Orchestras (GB)
Industry, innovation and infrastructure
Openalex Percentile: Top 4%
Digital Transformation in Industry
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.