Optical DWDM networks have long been designed to transport data as efficiently and reliably as possible. But a new class of innovation is emerging that turns these same networks into something far more powerful: intelligent sensing systems. At the forefront of this transformation is optical network tomography, a technology invented by Nokia Bell Labs that extracts actionable insights directly from the signals already flowing through fiber.
Rather than requiring new probes or measurement equipment, tomography leverages advanced algorithms applied to existing transponders to “see” inside the network—revealing its physical characteristics, detecting anomalies, and enabling a new level of operational intelligence.
But does what appears to be a promising concept on paper actually hold up in real-world conditions? To find out, Nokia conducted an extensive field trial across a network spanning three countries and more than 2,000 kilometers, in collaboration with CSC – IT Center for Science in Finland, Sikt –the Norwegian Agency for Shared Services in Education and Research, and SUNET – the Swedish University Computer Network.
Turning signal distortion into intelligence
At its core, tomography is based on a simple but powerful idea: every optical signal carries a trace of the journey it has taken. As light travels through fiber, it experiences distortions: dispersion, attenuation, amplification, and other physical effects. Traditionally, these impairments are simply compensated for to ensure error‑free transmission.
Tomography takes this one step further. By analyzing these distortions at the receiver, it reconstructs what happened along the path. In practical terms, this means operators can infer key network characteristics such as:
- Quantity of spans, their lengths and fiber types
- Number, position and type of amplifiers
- Signal power evolution across the link
- Localized losses or degradations
Crucially, this is achieved without prior knowledge of the network topology and using only the received signal data. The result is a detailed span-by-span view of the network – essentially an ‘optical topological map’ reconstructed from the signal itself.
A breakthrough in operational efficiency
One of tomography’s most compelling advantages is that it requires no additional hardware or dedicated measurement signals. The same data streams used for communication are reused for tomographic sensing. This dual‑use approach transforms deployed infrastructure into a monitoring system at virtually zero incremental cost with the use of coherent transmission technology already in use in the optical transport network.
This simplicity makes tomography particularly attractive for large and complex networks, completing the loss and localization information provided by optical time domain reflectometers (OTDR) – primarily used in optical line systems for detecting issues such as fiber cuts – enabling topology insights where it is most impactful and for prompting maintenance.
It also enables continuous and scalable insight across the network, rather than point‑in‑time measurements.
Key use cases across network operations
The ability to reconstruct and monitor the optical layer unlocks a wide range of use cases, spanning discovery, optimization, and security for an operator of a critical DWDM network, might it be a telco service provider, webscale, cloud provider, mission-critical enterprise or a public sector company.
- Network discovery and inventory
Tomography allows operators to automatically map unknown or partially known networks. This is especially valuable in scenarios such as leased spectrum, Managed Optical Fiber Networks (MOFN), or alien wavelength deployments. The leased network infrastructure is not a “black box” any longer for a network operator. Instead of relying on incomplete documentation or limited information from a service provider, network operators can generate accurate, up‑to‑date topology inventories of fiber types, span lengths, and amplifier configurations by leveraging the optical tomography insights. They can also detect when and how the topology changes due to various operational reasons (e.g., traffic was re-routed to a resilient path due to a cable break).
- Continuous monitoring and optimization
By repeatedly analyzing the same links over time, tomography enables trend analysis and predictive maintenance. Operators can track amplifier performance, detect drift in gain levels, and identify early signs of degradation. This allows proactive interventions before issues impact service quality.
- Anomaly detection and troubleshooting
Tomography can detect unexpected losses or anomalies along the fiber path, down to small attenuation events of just a few tenths of decibels. This capability enables faster troubleshooting and more precise fault localization—critical for reducing downtime and improving operational efficiency.
- Service enablement and network planning
Before deploying new services, operators can use tomography to verify and fine‑tune end‑to‑end network characteristics. This improves planning accuracy, network performance and reduces the risk of service degradation, particularly in multi-vendor open optical line system environments where visibility is limited.
A powerful new tool for network security
One of the most promising applications of tomography lies in physical layer security.
In shared or leased networks, operators often have little visibility into the underlying infrastructure. Tomography changes this by enabling continuous monitoring of signal behavior along the path.
For example, sudden or temporary losses in signal power could indicate physical intrusion—such as fiber tapping or unauthorized access. By detecting these anomalies in real time, tomography provides an additional layer of security, complementing traditional encryption and cybersecurity measures. In a world where optical infrastructure is increasingly considered critical, this capability represents a significant step forward.
Results from the field trial with CSC, Sikt and SUNET
The live trial demonstrated that the digital model derived from the tomography data closely aligned with the measurements made on the physical multi-span optical network with different fiber types, almost perfectly reflecting fiber types and span lengths across the entire route.
This field trial brought a clear demonstration that tomography delivers several key benefits:
- No additional infrastructure required: Works with existing transponders and live traffic
- Full‑path visibility: Reconstructs the network without prior topology knowledge
- Improved operational efficiency: Automates discovery, monitoring, and troubleshooting
- Enhanced resilience: Enables early detection of faults and degradations
- Stronger security posture: Detects potential physical threats in the optical layer
Together, these advantages could make tomography a highly scalable and cost‑effective solution for modern optical networks. Of course, further development is needed before tomography becomes fully operational. The algorithms must be hardened for diverse network conditions and multi‑vendor environments. Real‑time, scalable software integration with existing operational systems is essential. Correlating tomography with other monitoring tools will strengthen overall visibility. These steps will ensure a reliable, operator‑ready solution.
From connectivity to intelligence
As networks continue to evolve, the role of infrastructure is expanding. Optical systems are no longer just conduits for data—they are becoming platforms for insight that can be further enhanced when used in AI-driven automation. Tomography illustrates this shift clearly and is an optical domain tool that can enrich future autonomous networks. Tomography will play a critical role in enabling smarter, more secure, and more autonomous networks, where the infrastructure not only carries information but understands it. By turning unavoidable signal impairments into a source of intelligence, it transforms the network itself into a sensor – one that can sense and learn in real time.
While still maturing toward full deployment, the results from early field trials demonstrate its potential: accurate reconstruction of network topology, detection of subtle anomalies, and new levels of operational visibility.
