As climate change continues to influence weather patterns, communities are facing extreme events such as hurricanes, droughts, floods, and severe storms with increasing frequency and intensity. When disasters occur, disruptions to electricity, communications, transportation, healthcare, and emergency services can magnify the damage and make recovery more difficult. Ensuring that these critical systems remain operational during emergencies is therefore becoming an increasingly important priority.

Climate projections indicate that many extreme weather events are likely to become more intense in the coming decades. This makes advance planning essential for governments, emergency organizations, infrastructure operators, and other decision-makers seeking to reduce the impact of weather-related disruptions on services that communities depend on.

As a provider of connectivity infrastructure, Nokia operates across many of these essential sectors, including energy, transportation, healthcare, telecommunications, and emergency response. Reliable communications provide the foundation that enables these different systems to exchange information and coordinate their activities when a crisis occurs.

A resilient community depends on several interconnected elements working effectively together:

These components are closely linked, meaning that the failure of one can trigger problems elsewhere. For instance, an extended electricity outage can interrupt wastewater treatment and drainage systems, potentially increasing flood-related risks. Similarly, a communications failure can prevent residents from receiving warnings and make it harder for first responders to coordinate their activities.

Past disasters in the United States illustrate the consequences of such interconnected failures. Hurricane Sandy in 2012 resulted in widespread power outages across 18 states and disrupted wastewater infrastructure, contributing to additional flooding and public-health concerns. During Hurricane Helene in 2024, responders in Tennessee used an alternative statewide radio system to coordinate a helicopter rescue after conventional radio communications became unavailable.

The growing dependence on communications infrastructure is placing greater expectations on telecommunications companies to maintain connectivity during emergencies. Five major factors are contributing to this shift:

Recent incidents have demonstrated the consequences of inadequate network resilience. The 2025 electricity blackout across the Iberian Peninsula, for example, affected more than 50 million people in Spain and Portugal, with some areas experiencing outages lasting as long as 16 hours. The failure disrupted numerous critical services at a time when communications were particularly important. Telecommunications networks were also heavily affected, with internet traffic reportedly falling by approximately 90% in Portugal and 80% in Spain. In some areas, mobile connectivity was lost after backup power systems were exhausted.

The blackout also generated criticism of telecommunications operators over preparedness and dependence on conventional electricity supplies. Subsequent severe weather events further highlighted the vulnerability of infrastructure across the region.

Storm Claudia caused flooding and electricity disruptions in parts of Portugal and Spain, while Storm Kristin subsequently caused widespread damage in Portugal, leaving more than one million people without electricity and bringing down thousands of telecommunications and electricity poles. In response to these vulnerabilities, Spain announced plans requiring telecommunications providers to maintain a minimum of four hours of mobile service during power outages. Portugal's telecommunications regulator has also recommended additional requirements aimed at increasing network autonomy.

The challenge extends well beyond Europe. Telecommunications infrastructure in remote parts of North America has also been exposed to extreme weather risks. Canada's 2024 wildfires, for example, highlighted connectivity limitations affecting rural and remote communities.

Research conducted by Nokia in collaboration with CGI, examining disasters recorded between 2015 and 2025, found that among U.S. states with a Nokia presence, Texas recorded 87 disasters during that period, followed by Missouri with 61 and Oklahoma with 58. The frequency and combination of these events reinforce the need for telecommunications providers to prepare for disruption before disasters occur rather than relying solely on recovery measures afterward.

Future connectivity infrastructure will need resilience to be considered from the earliest stages of planning. This means incorporating resilience into network design, deployment, day-to-day operations, maintenance, and restoration strategies.

For telecommunications companies, stronger network resilience can reduce exposure to future disruptions while helping ensure that electricity, healthcare, emergency response, transportation, and other essential services remain connected. Ultimately, strengthening communications infrastructure also contributes to the resilience of the communities that rely on it.

Nokia is working with telecommunications companies and other organizations responsible for mission-critical services to strengthen resilience across entire infrastructure ecosystems. Its approach extends beyond basic connectivity, incorporating network infrastructure, sensing and detection technologies, power continuity, edge computing, and other capabilities that can help essential organizations remain operational during emergencies.

Artificial intelligence is becoming an important component of resilient infrastructure strategies. Within telecommunications networks and across interconnected infrastructure ecosystems, AI can support applications ranging from climate-risk analysis to automated network recovery and the prioritization of resources during emergencies.

Technologies such as Nokia's AI-RAN platform and MantaRay SON can help identify potential network problems and enable corrective action before service is significantly affected. AI-powered digital twins can also help communities and infrastructure operators model potential scenarios, locate vulnerabilities, evaluate risks, and implement improvements before an actual disaster occurs.

Resilience cannot be achieved by telecommunications companies alone. Governments, utilities, emergency services, healthcare providers, technology companies, community organizations, and other infrastructure stakeholders all have roles to play in preparing for increasingly complex climate-related disruptions.

Nokia's participation in New York City Climate Week from September 20–24 is focused on discussions around resilience and collaboration across these interconnected ecosystems. The company is also participating in a CGI Climate Working Session on Digital Resilience, with further initiatives and actions expected to emerge from these discussions.

For decades, fiber-optic networks have primarily been viewed as high-performance infrastructure whose main purpose was to reliably transport data. As mission-critical organizations place greater emphasis on resilience, cybersecurity, and operational visibility, that role is changing. Fiber is increasingly becoming a source of real-time information about conditions around the network itself.

The move from conventional fiber monitoring to fiber sensing represents a significant shift in how optical networks can deliver value. Traditional monitoring generally informs operators after performance has deteriorated or a connection has failed. Fiber sensing goes further by continuously identifying activity and changes occurring along and near the fiber route, potentially providing warnings before communications are disrupted. When combined with artificial intelligence, these physical signals can be interpreted and transformed into timely, actionable information.

Why Fiber Sensing Matters for Utilities
The importance of this capability is particularly evident in mission-critical sectors such as electric utilities. In these environments, communications infrastructure is an integral part of the power grid rather than simply a system for supporting conventional business applications.

Reliability requirements are extremely high. Consumers expect electricity to remain available whenever they need it, which means utilities require communication networks capable of delivering both high availability and resilience. They also increasingly need technologies that can identify emerging problems rather than simply report outages after they occur.

Fiber sensing can contribute to this objective by turning existing optical infrastructure into an early-warning mechanism. Abnormal activity along a fiber route can potentially be identified before it develops into a communications failure or security incident. This makes optical-network modernization about more than capacity; it can also improve visibility, automation, and resilience.

What Fiber Sensing Can Detect
Traditional monitoring largely asks whether a network connection is operating correctly. Fiber sensing changes the question to: what is happening along the network corridor?

That distinction can have important operational consequences. For example, instead of discovering that a fiber cable was severed at 2:14 p.m., a sensing-enabled system could potentially identify unusual activity several minutes earlier that resembles construction or excavation approaching a protected right-of-way.

Such advance warning could give an operator time to contact workers at the location, halt potentially damaging activity, send security or law-enforcement personnel, or deploy a fiber maintenance team. Avoiding an outage is only part of the benefit. Earlier intervention can also reduce repair expenses, minimize operational disruption, and help critical infrastructure providers maintain the continuity their customers depend on.

Modernizing Utility Networks: Key Priorities
Utility network upgrades typically focus on several fundamental requirements: availability, predictable performance, automation, security, and compliance.

High availability is essential because communications failures can introduce risks to grid operations. Predictable network performance is equally important, although its significance may be less obvious. Equipment installed at substations and on utility poles can depend on precise timing and very low levels of jitter. A network may appear to be functioning normally while still operating outside the tolerances required by protection and control equipment.

Fiber sensing provides another layer of visibility by helping operators recognize developing problems before they reach a point where network performance or grid operations are affected.

Improving Operations Through Faster Root-Cause Analysis
Fiber sensing can also improve operational efficiency. Conventional fault investigations can require considerable time and resources. An alarm may result in technicians being sent to the field, where they must search for the underlying problem, sometimes only after service has already been interrupted.

Sensing adds valuable context by helping determine what happened, where it happened, and how the event is developing. More accurate location information can allow field teams to reach the problem faster and spend less time searching along extensive fiber routes.

For utilities operating with limited budgets and field resources, reducing unnecessary site visits and shortening troubleshooting time can provide meaningful operational benefits.

Fiber as a Distributed Security Sensor
The potential security applications of fiber sensing extend beyond accidental damage and environmental events. Critical infrastructure operators must also consider deliberate tampering, unauthorized access, and physical intrusion.

Fiber routes frequently pass through remote locations or connect directly to sensitive facilities. Sensing technology can potentially identify physical activity near the fiber, including events associated with doors opening, footsteps, excavation, or other acoustic and vibration signatures.

This changes the role of the communications network. Instead of functioning solely as a means of transmitting information, the fiber can also serve as a distributed sensing layer, providing continuous awareness along physical corridors that may otherwise be difficult or expensive to monitor with conventional security equipment.

From OTDR to Advanced Optical Signal Analysis
The technology behind fiber sensing has developed considerably. Earlier approaches typically monitored optical power at the endpoints of a transmission path and responded when significant changes occurred.

Optical time-domain reflectometers, or OTDRs, introduced a more detailed diagnostic capability. These instruments send pulses of light through the fiber and analyze the resulting backscatter, using a principle broadly comparable to radar. This allows operators to locate cable breaks and other forms of degradation with increasing accuracy, in some cases down to a few meters.

Modern fiber sensing builds on these capabilities by analyzing much smaller changes in optical backscatter and other characteristics of the transmitted signal. Information such as changes in optical phase and polarization can provide insight into physical events occurring along the fiber, including events that have not yet caused an interruption in service.

Coherent Optics and Digital Signal Processing
Two technological developments have helped make advanced sensing practical on a larger scale.

The first is the widespread adoption of coherent optical technology. Coherent receivers, which were once expensive and specialized, are now common components of modern optical networking systems. They can extract substantially more information from an optical signal than earlier technologies.

The second is the rapid advancement of digital signal processing (DSP). Modern DSP capabilities can analyze extremely small variations in the behavior of light traveling through optical fiber and relate those changes to physical activity in the surrounding environment.

Together, coherent optics and DSP improve the ability of fiber systems to detect phenomena such as vibration, seismic activity, and temperature changes.

AI Turns Sensing Data Into Actionable Intelligence
Detecting physical activity is only the beginning. The greater challenge is interpreting the enormous volume of information generated by sensitive sensing systems.

Fiber routes naturally experience continuous background activity. Vehicles, pedestrians, industrial equipment, scheduled maintenance, and other routine events can all generate vibrations or other signals. Treating every detected change as an alarm would quickly make the system impractical.

This is where AI becomes an important enabling technology. Artificial intelligence can help distinguish meaningful events from normal background activity, turning sophisticated sensing capabilities into information that operators can use to make operational decisions.

Establishing What “Normal” Looks Like
AI-based fiber sensing can learn the normal patterns associated with individual locations and environments. There is no single definition of normal activity across an entire network.

For example, a fiber running beside a busy road will naturally experience different vibration patterns from one passing through a remote rural area. Similarly, a location with planned maintenance during particular hours should be treated differently from a restricted facility where activity is unexpected outside authorized periods.

Machine-learning models can identify recurring patterns, reduce false alarms, and incorporate contextual information such as maintenance schedules, approved work orders, and regular environmental noise.

As these systems learn over time, they can become better at separating routine activity from potentially significant anomalies. Greater confidence in alerts is particularly important when detections may trigger automated processes or the deployment of security personnel.

Detecting Problems Before They Become Failures
AI can also support a more proactive approach to network management. Rather than waiting for a predefined threshold to be exceeded, analytical systems can identify patterns that may indicate an approaching failure or security event.

The practical benefit is additional response time. For mission-critical infrastructure, that extra time can affect more than operating costs—it can contribute to safety, service continuity, and overall resilience.

Adding Intelligence Without Sacrificing Network Capacity
Introducing sensing capabilities does not necessarily require a significant reduction in communications bandwidth. In many implementations, fiber sensing can make use of information already available from optical transceivers and receivers during normal network operation.

The additional requirements are generally associated with measurement, data processing, analytics, and software integration rather than consuming large amounts of transmission capacity.

This means sensing can potentially operate alongside regular communications traffic, allowing organizations to extract additional value from fiber infrastructure that is already carrying operational and business data.

Modern Optical Infrastructure Enables Advanced Sensing
Many of these capabilities are closely connected to the adoption of coherent optical detection, which is now common across modern optical networking platforms. Fully utilizing advanced sensing capabilities, however, may require up-to-date optical infrastructure.

Organizations operating equipment that is several decades old may not be able to access the full potential of modern sensing until their optical platforms are upgraded.

Nevertheless, optical modernization is already being driven by other requirements. Growing bandwidth consumption, expanding data centers, and increasing demand from AI computing are pushing organizations toward higher-capacity optical transport. When equipment is upgraded to improve throughput, latency, and flexibility, fiber sensing can potentially be incorporated as an additional capability at a comparatively modest incremental cost.

Getting More Value From Existing Fiber
The broader strategic opportunity is to increase the value delivered by existing fiber assets.
A single optical network can transport grid telemetry, protection traffic, and enterprise communications while also providing information that helps protect the physical fiber corridor, identify emerging threats, and improve field operations.

For utilities modernizing their networks to support renewable-energy integration and broader decarbonization objectives, protecting critical infrastructure and improving operational efficiency can support that wider transformation.

AI plays an important role by converting complex, location-specific optical signals into information that operators can understand and act upon.
Fiber Sensing as a Future Network Capability

The role of fiber sensing is likely to become increasingly important when organizations design and modernize mission-critical optical networks. Infrastructure intended to remain operational for a decade or longer may need to be evaluated on more than capacity, latency, and manageability. Resilience, physical security, and sensing capabilities can also become important considerations.

As sensing technology improves its sensitivity and ability to pinpoint events, and AI systems become more effective at filtering background noise and recognizing significant patterns, fiber sensing has the potential to move from a specialized capability toward a standard feature of modern optical networks.

The result is a fundamentally more capable communications infrastructure: a network that not only transports data but can also observe activity around the fiber route and provide operators with information that may help address emerging problems before they develop into major failures.

Expanding access to artificial intelligence (AI) in rural communities is among the issues I look forward to exploring at DISTRIBUTECH International 2026 in San Diego. As AI adoption accelerates, an important question is emerging: how significant could this opportunity become for power utilities?

Governments, electric utilities and telecommunications companies are working to reduce the digital divide. Reliable broadband has become fundamental to modern life, supporting education, healthcare, economic activity and participation in the wider digital economy.

However, serving rural communities requires more than extending fiber to individual homes and businesses. The underlying infrastructure must include resilient and modern optical transport networks capable of carrying today's digital traffic while also supporting the rapidly increasing data requirements of AI applications.

Utilities Are Expanding Their Fiber Footprint
Power utilities are increasingly installing fiber along transmission and distribution infrastructure. These networks can provide real-time visibility into grid operations, secure communications with substations and reliable connectivity for corporate networks. In some cases, utilities are also using their fiber infrastructure to support broadband services.

To handle these increasingly diverse workloads, utility networks need to be scalable, secure, straightforward to operate and highly resilient. Developments in coherent optics, compact networking equipment, switching, automation and encryption are making this increasingly practical.

Modern optical platforms can combine critical operational technology (OT) traffic with demanding IT workloads and expanding broadband requirements over middle-mile infrastructure.

Data Center Interconnect Opens a New Revenue Stream
A particularly attractive opportunity lies in data center interconnect (DCI).

The rapid growth of AI is driving enormous demand for fast, low-latency connections between data centers. Power utilities are well positioned to participate because many already control access to substantial power resources and operate fiber routes extending across long distances.

This infrastructure could allow utilities to offer high-capacity DCI services at speeds such as 100, 400 or 800 Gb/s.

Managed optical fiber networks (MOFNs) could serve cloud providers, AI companies and large enterprises seeking dedicated, high-performance connectivity. As data centers move closer to the edge of the network, they can also support AI inference applications for consumers and businesses.

That shift creates additional demand for greater bandwidth, reduced latency and interconnection solutions that provide stronger control over where data is processed and stored.

AI Is Creating Unprecedented Network Demand
The rapid adoption of AI is fundamentally changing the requirements placed on communications infrastructure. Businesses are increasingly experimenting with agentic AI to automate processes, while technology companies continue to make enormous investments in computing infrastructure.

Goldman Sachs has projected that AI-related spending will surpass US$500 billion in 2026. Deloitte has estimated that electricity demand from AI-focused data centers in the United States could increase by more than 30 times by 2035. McKinsey has also projected that AI could account for as much as 70% of data center capacity demand by 2030.

Such growth will put significant pressure on optical transport and DCI infrastructure. Networks that were designed for earlier generations of cloud and enterprise traffic will increasingly need upgrades to handle AI-driven capacity requirements.

Why Rural Locations Are Becoming Attractive to Data Centers
The limitations of major urban markets are another factor pushing data center development toward rural areas. Cities and established technology hubs increasingly face constraints involving available land and electricity capacity. In some locations, obtaining a new data center power connection can take several years.

Rural markets can offer a different set of advantages, including more affordable land, access to renewable energy, greater availability of water, cooler weather in some regions, lower exposure to certain natural hazards and convenient access to long-distance fiber routes.

Major cloud and technology companies, including AWS and Microsoft, are consequently expanding data center investments into less densely populated areas.

Creating Networks Ready for AI and Multiple Services
The latest generation of optical networking equipment is becoming increasingly compact, modular and energy efficient. Programmable coherent pluggables can support data rates ranging from 100G to 800G over distances of thousands of kilometers, reducing the need for expensive regeneration infrastructure.

For utilities, these capabilities can lower energy consumption per transmitted bit while providing a more practical migration path toward emerging 800G routing platforms. They can also support open, multivendor network architectures.

Rather than maintaining separate infrastructure for different applications, utilities can build multi-purpose networks capable of handling broadband aggregation, middle-mile connectivity, IT/OT convergence and new DCI and MOFN services.

Modern architectures can also reduce equipment footprints and operating costs compared with legacy platforms while providing stronger automation, network protection and operational flexibility.

A Strategic Opportunity for Rural Utilities
The convergence of rural connectivity, power infrastructure, fiber networks and AI creates an important opportunity for utilities.

Broadband deployment will remain critical for rural communities, but AI is introducing an entirely new source of demand for high-capacity connectivity. Many rural locations already possess some of the fundamental ingredients needed for next-generation data center development: available land, access to electricity and proximity to long-haul fiber.

By investing in modern, high-capacity optical transport infrastructure, utilities can potentially serve multiple markets simultaneously—from broadband providers and enterprises to cloud companies and AI data centers.

This could position power utilities not simply as providers of electricity, but as important participants in the digital infrastructure supporting the next generation of AI.

I look forward to exploring these developments and sharing more insights at DISTRIBUTECH International 2026 in San Diego. For those unable to attend, I will continue sharing developments and perspectives from this rapidly evolving industry.

Telecom providers are pushing for fast 6G standardization and quick adoption across enterprise and consumer markets, with AI playing a central role.

The transition to 6G marks a major shift — potentially becoming the first generation of wireless networks built to be AI-native. Because AI will deeply influence how 6G operates, engineers face a new challenge: validating systems that are far more adaptive, intelligent, and fast than previous generations.

This overview outlines how AI can support 6G design validation for teams in communication service providers, mobile operators, technology vendors, and device manufacturers. It also explores emerging applications enabled by AI and 6G, the types of AI techniques involved, and how these tools can streamline design and testing workflows.
 
What new opportunities will 6G and AI unlock?
AI and 6G together are expected to drive major innovations, including real-time digital twins, advanced manufacturing systems, highly autonomous transport, holographic communication, and widespread edge intelligence. These capabilities align with the visions of the ITU and 3GPP for 2030 and beyond.

Real-time digital twins
With widespread coverage, extremely low latency, and high throughput, 6G paired with AI could create high-fidelity, real-time digital counterparts of physical assets and environments. These digital twins would support modeling, control, analysis, and simulation with unprecedented accuracy. Digital twin networks could mirror actual network conditions to enable continuous optimization, especially when combined with integrated sensing and communication (ISAC).

Smart factories
AI-enhanced 6G connectivity could enable industrial automation at scale through reliable, ultra-responsive data exchange across robotics, industrial IoT, and intelligent devices. “Industrial 6G” may enable fully automated operations in environments such as factories, ports, and airports, supported by private 6G deployments.

Autonomous mobility
Next-generation mobility systems — from autonomous vehicles to intelligent transportation — will rely on AI-powered 6G capabilities. This includes AI-assisted driving, real-time mapping, and precise positioning for cellular vehicle-to-everything (C-V2X) interactions.

Holographic communication
Future 6G and AI infrastructure may support immersive communications such as holographic telepresence and multi-sensory remote interaction. AI-driven semantic communication could reduce bandwidth demands by transmitting only the essential meaning behind data-heavy content.

Distributed edge intelligence
6G is expected to blur the line between communication and computing by pushing AI models to the network edge. This could enable coordinated inference, collaborative robotics, and pervasive, real-time intelligence across devices.
 
How will AI improve 6G network design and operations?
6G will involve both physical elements (e.g., radios, base stations, user devices) and logical components (e.g., RAN, core network functions, protocol stacks). Many of these will be optimized using AI during design, validation, and even runtime.

AI-native air interface
AI could enhance key radio functions such as channel estimation, symbol detection, beam selection, modulation, and antenna configuration. These models may operate on devices, at the base station, or jointly across both.

AI-assisted beamforming
AI methods may support:

AI-optimized RAN
AI could enable a self-organizing RAN capable of real-time adaptation, end-to-end optimization, and autonomous performance tuning.

Automated network management
AI-driven operations may include predictive maintenance, traffic forecasting, energy optimization, and intelligent resource allocation. Real-time threat detection and mitigation could also be enhanced through AI analytics.
 
Which AI techniques are most useful for validating 6G performance?
A range of AI methods — deep learning, reinforcement learning, generative models, and more — will support system-level design and testing.

Reinforcement learning (RL)
RL is well-suited for automating decision-making in unpredictable environments and may be applied to:

Deep neural networks (DNNs)
DNNs may support tasks such as:

Transformer models
Transformer autoencoders may enhance CSI compression and feedback efficiency.

Graph neural networks (GNNs)
GNNs can model network topology and spatial relationships for interference control, mobility forecasting, and resource allocation.

Generative adversarial networks (GANs)
GANs can generate realistic channel data, support denoising, and detect anomalies.

Large reasoning/action models
These emerging agentic models may coordinate complex workflows and help test sophisticated, multi-component 6G systems.
 
How will synthetic AI data support 6G testing and validation?
AI-generated data will be crucial for exploring the huge range of possible 6G conditions — many of which cannot be physically tested early on.

Key synthetic-data methods include:

 
Can AI help validate 6G hardware and chip designs?
Yes. AI-powered anomaly detection, automation, and data-driven modeling could support the design of components for sub-THz frequencies, UM-MIMO, and other 6G features.
Key methods include:

 
What challenges come with using AI for 6G validation?
AI’s reliability isn’t guaranteed. Issues include out-of-distribution errors, limited data, low interpretability, overfitting, and hallucinations. To improve trustworthiness:

 
Keysight’s role
This summary illustrates how AI can support 6G design and testing. Keysight provides tools, research expertise, and 6G-ready test solutions to help engineering teams innovate with confidence throughout development.

Click here to know more.