Cloud Native Telco Transformation Insights from T-Systems

The journey to becoming a cloud-native telco is not just a buzzword exercise. It requires a full-scale transformation of networks, business models, and operating cultures. At Mobile Europe’s Becoming a Cloud-Native Telco virtual event, Richard Simon, CTO at T-Systems International, outlined how telcos are grappling with this change, sharing insights from both successes and ongoing challenges.

Cloud-native is not just about adopting containers or orchestrating with Kubernetes. Richard Simon described it as a maturity model that demands strategic vision, architectural readiness, and cultural shift. The telco industry, long rooted in proprietary systems, is gradually moving towards software-defined infrastructure. Network Function Virtualisation (NFV) remains foundational, enabling operators to decouple traditional monolithic services and deliver them in a modular, digital-native manner—whether in private data centres or public clouds.

The industry is also seeing the rise of platform engineering. This evolution builds on DevOps and site reliability engineering (SRE) to create standardised internal developer platforms. These platforms reduce cognitive load for developers, increase consistency in toolchains and workflows, and enable a shift-left approach for operations and security. It is a critical step towards making innovation scalable and repeatable within telcos.

The cloud-native ecosystem has exploded in scope, with the CNCF landscape illustrating the diversity and maturity of open source components now in use. Telcos, once cautious about community-led projects, are now not only consuming but also contributing to open source. This openness is pivotal for achieving agility and interoperability in a multivendor environment.

With the increasing complexity of hybrid and multicloud strategies, avoiding vendor lock-in has become essential. Richard highlighted how telcos are optimising costs, improving resilience, and aligning workloads with the most suitable cloud environments. Multicloud is no longer a theoretical construct. It is operational reality. But with it comes the need for new thinking around cloud economics.

Cloud financial operations are no longer limited to cost tracking. They now include strategic frameworks (FinOps), real-time cost management, and a growing focus on application profiling. Profiling looks at how software consumes cloud resources, guiding developers to write more efficient code and enabling cost-effective deployments.

While generative AI dominates headlines, the pace of adoption in telco is deliberate. Richard pointed out that while investment in GenAI is widespread, only a small fraction of deployments have reached production. Most telcos are still in proof-of-concept or pilot phases, reflecting the technical and regulatory complexity involved.

Unlike some sectors, telcos face strict compliance requirements. GenAI inference stages—when customer data is processed—raise concerns around data sovereignty and privacy. As a result, telcos are exploring how to balance innovation with responsibility. Some are experimenting with fine-tuning foundational models internally, while others prefer to consume GenAI as a service, depending on use cases ranging from network automation to document processing.

GenAI is a high-performance computing (HPC) workload. Training large models requires significant infrastructure, making decisions around build versus buy critical. Richard outlined three tiers of AI adoption: infrastructure-as-a-service for DIY approaches, foundation-model-as-a-service for fine-tuning, and software-as-a-service for fully hosted solutions. Each tier comes with trade-offs in control, cost, and complexity.

Looking ahead, three themes stood out in Richard’s conclusions.

First, the era of AI agents is beginning. These autonomous systems, capable of reasoning and acting across complex tasks, will be the next experimentation frontier. Pilots in 2025 and 2026 will pave the way for a broader agentic AI economy.

Second, cloud economics continues to evolve. Operators must invest in cost visibility and governance rather than reactively scaling back cloud usage. The emergence of profiling and observability tooling is helping align cost with performance and business value.

Third, sovereignty is rising in importance. Telcos must ensure control over data and infrastructure, not only to comply with regional regulations but also to maintain intellectual property and operational resilience. Sovereign cloud models, abstracted control planes, and localised inference infrastructure are becoming strategic imperatives.

His complete talk is embedded below:

The cloud-native journey is not linear. It requires operators to architect for modularity, align with open ecosystems, and stay grounded in real-world economics. As Richard Simon’s keynote showed, the transformation is well underway, but its success will depend on how telcos integrate cloud, AI, and sovereignty into a coherent and adaptable strategy.

Related Posts: 

• The 3G4G Blog: What is Cloud Native and How is it Transforming the Networks?
• Operator Watch Blog: Cloud Native Progress and Pain Points According to Orange
• The 3G4G Blog - Lessons from ANRW ’24: AI and Cloud in 5G/6G Systems
• The 3G4G Blog: Disaggregation of 5G Core (5GC) Network
• The 3G4G Blog: Reigniting Growth in the Telecom Industry with AI and Cloud
• The 3G4G Blog: Transitioning from Cloud-native to Edge-Native Infrastructure
• Free 6G Training: Cloud Native Architecture & Migration Towards 6G from B6GS 2023
• Free 6G Training: SK Telecom Claims ‘Bare Metal-Based Cloud-Native 5G Core’ as Foundation for 6G Evolution 

A Beginner’s Guide to the 5G Air Interface

Some of you may know that I manage quite a few LinkedIn groups, and I often come across advanced presentations and videos on LTE and 5G. From time to time, people reach out asking where they can access a free beginner-level course on the 5G Air Interface. With that in mind, I was pleased to see that Mpirical have shared a recorded webinar on YouTube titled Examining the 5G Air Interface, which seems ideal for anyone looking for a basic introduction.

Philip Nugent, Senior Technical Trainer at Mpirical, explains some of the key terms, concepts and capabilities of 5G New Radio. The webinar provides a high level overview of several important topics including 5G frequency bands and ranges, massive MIMO and beamforming, protocols and resources, and a look at the typical operation of a 5G device. It concludes with a short trainer Q&A session.

The video is embedded below:

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3GPP Release 18 Signal level Enhanced Network Selection (SENSE) for Smarter Network Selection in Stationary IoT

As 5G evolves and the number of deployed IoT devices increases globally, efficient and reliable network selection becomes ever more critical. Particularly for stationary devices deployed in remote, deep-indoor or roaming environments, traditional selection mechanisms have struggled to provide robust connectivity. This has led to operational challenges, especially for use cases involving low-power or hard-to-reach sensors. In response, 3GPP Release 18 introduces a new capability under the SA2 architecture work, Signal level Enhanced Network Selection (SENSE), designed to tackle this exact issue.

In today’s cellular systems, when a User Equipment (UE), including IoT modules, switches on or recovers from a loss of coverage, it performs automatic network selection. This typically prioritises networks based on preferences such as PLMN priority lists and broadcast cell selection criteria, while largely ignoring the actual signal strength at the device’s location. This approach works reasonably well for mobile consumer devices that can adapt through user movement or manual intervention. However, for stationary IoT UEs, which are often unmanned and deployed permanently in locations with limited or fluctuating radio conditions, this method can result in persistent suboptimal connectivity.

The issue becomes most evident when a device latches onto a visited PLMN (VPLMN) with higher priority despite poor signal quality. The UE might remain connected to this weak network, struggling to maintain bearer sessions or repeatedly failing data transfers. These failures often go undetected by the operator's monitoring systems and may require expensive manual intervention in the field. The cumulative impact of such maintenance activities adds significantly to operational expenditure, especially in mass-scale IoT deployments.

SENSE aims to fix this problem by making signal level an integral part of the automatic network selection and reselection process. Rather than simply following preconfigured priority rules, UEs enabled with SENSE will now assess the received signal quality during network selection. This allows them to favour networks that offer stronger and more stable radio conditions, even if they have lower priority, when such conditions are essential for reliable connectivity.

The capability is particularly targeted at stationary IoT UEs that support NB-IoT, EC-GSM-IoT, or LTE Cat-M1/M2. These devices are often used in applications such as water level monitoring, power grid sensors, and remote metering, installations where physical access post-deployment may be difficult or even infeasible.

To implement SENSE, the Home PLMN (HPLMN) can configure the UE to apply Operator Controlled Signal Thresholds (OCST) for each supported access technology. These thresholds are stored within the USIM and define the minimum signal quality required for a network to be considered viable. The OCST settings can be provisioned before deployment or updated later via standard NAS signalling mechanisms, including the Steering of Roaming (SoR) feature.

When a SENSE-enabled UE attempts to select a network, it checks whether the signal level from any candidate network meets or exceeds the configured OCST for its supported radio access technologies. If it does, the UE proceeds to register with that PLMN. If no suitable network meets the signal thresholds, the UE falls back to the legacy selection process, which excludes signal strength as a factor. This dual-iteration method ensures backward compatibility while enabling more robust performance where SENSE is supported.

Additionally, SENSE influences periodic network reselection. If the average signal quality from a registered PLMN drops below the OCST threshold over time, the UE will proactively seek alternative PLMNs whose signals meet the configured criteria. This continuous evaluation helps avoid long-term connectivity issues that may otherwise remain unnoticed.

SENSE is not intended to disrupt roaming steering or PLMN preferences altogether. Instead, it introduces a smart, context-aware filter that empowers the UE to make better decisions when radio conditions are poor. By integrating signal level awareness early in the selection logic, operators gain a powerful new tool to reduce failure rates and minimise costly field maintenance.

As the IoT landscape expands across industries and geographies, features like SENSE will play a vital role in supporting dependable, scalable and autonomous deployments. In Release 18, 3GPP has taken a meaningful step towards improving network availability for devices that need to just work, no matter where they are.

Related Posts: 

• The 3G4G Blog: 3GPP Release 18 Description and Summary of Work Items
• The 3G4G Blog - 5G-Advanced Store and Forward (S&F): Enabling Resilient IoT Communications via Satellite
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• The 3G4G Blog: Small Data Transmission (SDT) in LTE and 5G NR
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An Introduction to OSS/BSS in Mobile Networks

When discussing mobile networks, much of the focus tends to be on radio access technologies, spectrum, or core network evolution. However, two often overlooked yet critical components in the operational backbone of any telecom network are Operations Support Systems (OSS) and Business Support Systems (BSS).

While not typically defined in 3GPP specifications, OSS and BSS play a vital role in keeping the network functional and the business viable.

OSS is responsible for network-facing tasks such as monitoring, configuration, fault management, and performance optimisation, commonly summarised using the FCAPS model: Fault, Configuration, Accounting, Performance, and Security. It ensures the network operates smoothly and supports the delivery of services.

BSS, on the other hand, deals with customer-facing functions like CRM, order handling, billing, and revenue management. It ensures that customers can purchase, use, and be billed accurately for services,  forming the foundation for business growth and customer satisfaction.

To help introduce this important topic, we’ve created a short video explainer that outlines:

• The basic architecture of OSS and BSS
• Their roles within a multi-vendor network
• Key interfaces such as EMS, NMS, and TMN
• Why OSS/BSS are critical for digital transformation and operational efficiency

The video is embedded below and the slides are available here:

While OSS/BSS may not be headline features in 5G or 6G discussions, they remain the unsung heroes that ensure networks are operational, customers are happy, and services are profitable.

Let us know your thoughts in the comments or on social media. We're always keen to learn from those who work closely with these systems.

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Towers, Masts and Poles: The Backbone of Telecom Infrastructure

We often walk past them without a second glance—towers, masts, and poles that quietly support the vast web of our modern telecommunications networks. But behind these unassuming structures lies a fascinating history and a critical role in enabling everything from phone calls to television broadcasts.

In a brilliant lecture hosted by the IET, Professor Nigel Linge (with support from Professor Andy Sutton) takes us on a journey through the evolution of telecom infrastructure. Starting from ancient beacons and Napoleonic-era semaphores to the iconic BT Tower and long wave radio transmitters, the talk connects the dots across centuries of innovation.

On Friday 28th March I delivered a lecture to #TheIET at Savoy Place in London on the subject of "Telecom Towers, Masts, and Poles". Celebrating one of the visible faces of the telecoms industry. It was recorded by IETtv and can be viewed here: https://t.co/2MULwtRp5x pic.twitter.com/aD4cK7zyZU

— Nigel Linge (@NigelLinge) April 7, 2025

The lecture touches on early telegraphy using bare copper wires strung on porcelain insulators, the dawn of voice telephony, Marconi’s pioneering wireless transmissions, and the growth of regional radio and TV broadcasting in the UK. It also highlights how microwave relays and horn-reflector antennas became vital to long-distance communication, with the BT Tower serving as a key hub in the national network.

Whether it’s the humble telegraph pole or the towering masts on hilltops, each structure plays a part in delivering connectivity. This presentation offers a timely reminder of the physical foundations of our digital world—often overlooked, yet essential to our everyday lives.

Watch the full lecture below:

You can also read an article by them detailing many things covered in the lecture here.

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Understanding ETSI’s Industry Specification Groups (ISGs) and Why They Matter

The European Telecommunications Standards Institute (ETSI) is a leading standards development organisation (SDO) recognised for producing globally applicable standards for ICT, including fixed, mobile, radio, converged, broadcast, and internet technologies. Based in Europe but with worldwide influence, ETSI provides an open and inclusive environment for industry players to collaborate on the development of future technologies.

A recent overview presentation of ETSI by Jan Ellsberger, ETSI's Director General, is available on the 3GPP website here.

ETSI's Industry Specification Groups (ISGs) are collaborative groups formed within ETSI to address emerging and often pre-standardisation topics in a flexible, fast, and open manner. They provide a platform for industry players, including companies, research organisations, and other stakeholders, to work together on technical specifications outside the constraints of formal standardisation processes.

Key Features of ISGs include:

• Focus on innovation: ISGs often tackle new or rapidly evolving technologies, such as Network Functions Virtualisation (NFV), Quantum Key Distribution (QKD), and AI.
• Open participation: Participation is open to ETSI members and non-members, although non-members pay a fee.
• Faster timelines: ISGs are designed to deliver results quickly, often within 12–24 months, making them well-suited for fast-moving domains.
• Flexible structure: They are less formal than ETSI Technical Committees, which allows more agile collaboration.

ISGs produce documents such as:

• Group Specifications (GS) – technical specifications that can later be taken up by formal standardisation bodies.
• Group Reports (GR) – informative reports including use cases, frameworks, or recommendations.

ISGs help shape the direction of future standards and industry practices by offering an open, collaborative environment for technical consensus. They often bridge the gap between research and standardisation.

Dr Howard Benn, a mobile industry veteran with contributions spanning from GSM to 5G, has created a short video on ETSI’s ISGs, embedded below:

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5G-Advanced Store and Forward (S&F): Enabling Resilient IoT Communications via Satellite

Introduction

As the deployment of 5G networks continues to expand globally, the industry is already looking ahead to enhance capabilities through 5G-Advanced features. Among these innovations is the "Store and Forward" (S&F) functionality for Non-Terrestrial Networks (NTN), which represents a significant advancement for IoT applications utilizing satellite connectivity. This feature, specified in 3GPP Release 19, addresses one of the key challenges in satellite communications: maintaining service continuity during intermittent feeder link connectivity.

What is Store and Forward?

Store and Forward (S&F) satellite operation is designed to provide communication services for User Equipment (UE) under satellite coverage without requiring a simultaneous active feeder link connection to the ground segment. This capability is particularly relevant for delay-tolerant IoT services utilizing Non-Geostationary Orbit (NGSO) satellites.

In simple terms, S&F enables satellites to:

• Collect data from IoT devices when they're in range
• Store this data onboard the satellite
• Forward the data to ground stations only when a connection becomes available

This approach fundamentally differs from traditional satellite operations, which require end-to-end connectivity at the moment of transmission.

Source: 3GPP TR 22.865: Technical Specification Group Services and System Aspects; Study on satellite access Phase 3;

Normal Operation vs. Store and Forward

To understand the significance of S&F, it's important to contrast it with the "normal/default satellite operation" mode:

Normal/Default Satellite Operation

In the traditional model, signalling and data traffic exchange between a UE with satellite access and the ground network requires both service and feeder links to be active simultaneously. This creates a continuous end-to-end connectivity path between the UE, satellite, and ground network.

Store and Forward Operation

Under S&F operation, the end-to-end exchange of signalling/data traffic is handled as a two-step process that doesn't need to occur concurrently:

• Step A: Signalling/data exchange between the UE and satellite takes place even without the satellite being connected to the ground network. The satellite operates the service link without an active feeder link connection, collecting and storing data from IoT devices.
• Step B: Later, when connectivity between the satellite and ground network is established, the stored communications are transmitted to the ground network.

This approach bears similarities to existing store-and-forward services like SMS, where end-to-end connectivity between endpoints isn't required simultaneously.

Source: Ericsson Blog

Technical Requirements for Store and Forward

The implementation of S&F relies heavily on regenerative satellite payloads, as opposed to transparent payloads. Here's why this distinction matters:

Regenerative Payload Advantages

A regenerative payload with an onboard gNB (next-generation NodeB) offers several critical capabilities:

• Onboard Processing: The ability to process and store data directly on the satellite
• Reduced Dependency: Less reliance on continuous ground segment connectivity
• Enhanced Resilience: The NTN can function even if the feeder link is temporarily severed
• Performance Improvements: Significant reductions in roundtrip time for all procedures between the gNB and UE

For S&F functionality, all or part of the core network functions must be placed on the satellite together with the gNB. This architectural change enables a new level of autonomous operation for satellite networks.

Applications for IoT

The Store and Forward capability is especially suited for delay-tolerant or non-real-time IoT applications. Examples include:

• Environmental Monitoring: Collecting sensor data from remote locations
• Asset Tracking: Monitoring the status of assets in transit through areas with limited ground infrastructure
• Agricultural Sensing: Gathering data from widely distributed sensors in rural areas
• Maritime and Offshore IoT: Supporting connected devices at sea where direct connectivity to ground networks is inconsistent

These use cases benefit from S&F's ability to ensure data is eventually delivered without requiring constant connectivity, which is particularly valuable for battery-powered IoT devices that need to conserve energy.

Relationship to Delay-Tolerant Networking

The concept of Store and Forward is well-established in delay-tolerant networking (DTN) and disruption-tolerant networking domains. These networking paradigms are designed to work in challenged environments where conventional protocols may fail due to long delays or frequent disruptions.

In the 3GPP context, S&F can be compared to SMS service, which doesn't require end-to-end connectivity between endpoints but only between the endpoints and the Short Message Service Centre (SMSC), which acts as an intermediate node handling storage and forwarding.

Future Implications

The introduction of S&F functionality represents an important step toward what Ericsson has called "data centers in the sky." By placing not just radio access network functions but also core network capabilities in space, we're moving toward satellite networks that can operate with greater autonomy and resilience.

This development also aligns with broader industry efforts to create truly global coverage through integrated ground and space networks. Combined with inter-satellite links (ISL), S&F enables more flexible and resilient network architectures that can maintain service even when individual links are unavailable.

Conclusion

Store and Forward represents a significant advancement in 5G-Advanced satellite communications, particularly for IoT applications. By decoupling the timing requirements between service link and feeder link communications, S&F enables more resilient, energy-efficient, and cost-effective deployment of IoT devices in remote or challenging environments.

As 3GPP Release 19 specifications continue to develop, we can expect to see this capability integrated into commercial satellite IoT offerings, expanding the reach of 5G networks to truly global coverage. While initially targeted at IoT applications, the architectural principles of S&F could eventually extend to other services, bringing us closer to ubiquitous connectivity across terrestrial and non-terrestrial networks.

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AI/ML in 3GPP: Progress, Challenges, and the Road to 6G

The ETSI Artificial Intelligence (AI) Conference – Status, Implementation and Way Forward of AI Standardization – took place from 5-7 February 2024 at ETSI, Sophia Antipolis, France. This in-person event provided a valuable platform for experts and peers to exchange insights, explore demos and posters, and discuss AI and Machine Learning (ML) within the Information and Communications Technology (ICT) sector.

The event agenda is available online, and all presentations can be accessed here.

AI/ML Work in 3GPP: Insights from Dr. Juan Montojo

Dr. Juan Montojo, a leading figure in 3GPP TSG Radio Access Networks (RAN) and rapporteur for the work item Artificial Intelligence/Machine Learning for NR air interface (NR_AIML_air), delivered an insightful presentation titled "Overview of AI/ML related work in 3GPP." His talk covered the current status of AI/ML in 3GPP and prospects as 6G priorities begin to take shape.

Further details are available in the 3GPP post and presentation.

Focus Areas in 3GPP AI/ML Work

Dr. Montojo outlined the critical focus areas for AI/ML within 3GPP:

• Infrastructure and Operator Control: Ensuring that operators maintain control over AI/ML implementations within their networks.
• Performance Monitoring: Establishing standards for monitoring AI/ML model performance, activation, and deactivation.
• Air Interface Extensions: Developing extensions to support AI/ML-specific use cases.
• Data Standards: Defining standardized processes for data collection, AI/ML model transfer, and delivery.
• Testing and Interoperability: Ensuring consistent device behavior and interoperability in AI/ML deployments.

Principles Guiding AI/ML in 3GPP

The AI/ML work in 3GPP is grounded in principles that echo regulatory frameworks like the European Commission’s AI Act:

• Data Security and Integrity: Safeguarding data confidentiality and ensuring integrity.
• Privacy and User Consent: Respecting data privacy and user anonymity, with explicit consent mechanisms.
• Operator Control: Empowering operators with control over data collection, transfer initiation, termination, and management.
• Future-Proof Design: Ensuring the system design is extendable to accommodate future advancements.

AI/ML Training Models: Current Practices and Future Directions

• Off-line Training: Currently, AI/ML models in 3GPP assume off-line training, where models are fully trained before deployment in commercial networks.
• On-line Training and Federated Learning: Future 6G developments may introduce on-line training. 3GPP WG SA2 is already exploring federated learning to enhance network automation.

Challenges and Opportunities for AI/ML in Cellular Networks

Dr. Montojo emphasized both the strengths and limitations of AI/ML in cellular networks:

• Strengths: AI/ML excels in tackling complex, non-linear problems that traditional methods struggle with. It enhances localized, data-driven decision-making.
• Challenges: High energy consumption remains a concern for both network and device sides. Standardization must balance flexibility with technical consistency.

The Path Forward: AI/ML in 6G

AI/ML is expected to become pervasive in 6G, influencing all aspects of system design and operation. Notable expectations include:

• Rel-21 Specifications: AI/ML will be incorporated from the outset, supporting evolving use cases and dynamic requirements.
• Flexible Standardization: Future specifications may be less rigid, enabling AI/ML to drive optimization through data-driven parameterization.

Conclusion

AI/ML's integration into 3GPP workstreams is advancing steadily, laying the groundwork for significant contributions to 6G networks. While AI/ML models themselves are not being standardized, the supporting frameworks around data collection, model management, and interoperability are set to shape the future of cellular technology.

For anyone invested in AI/ML's role in telecoms, understanding these foundational steps is essential as we move towards a more automated, intelligent, and adaptable network landscape.

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Reigniting Growth in the Telecom Industry with AI and Cloud

The telecom industry is at a crossroads. While demand for connectivity continues to surge, operators face stagnating revenues, rising costs, and increasing competition. In his keynote at the Brooklyn 6G Summit 2024, Manish Singh, CTO of Telecom Systems Business at Dell Technologies, outlined a compelling vision for how AI and cloud-native networks can reignite growth in the sector.

The Growth Challenge in Telecom

The traditional telecom business model is under pressure. Operators are struggling with:

• Revenue stagnation despite increasing data consumption.
• Rising operational costs driven by legacy infrastructure and inefficient processes.
• Intensifying competition from hyperscalers and alternative connectivity providers.

To overcome these challenges, Manish argues that telcos must embrace AI-native and cloud-native architectures as fundamental enablers of transformation.

AI: The Catalyst for Intelligent Networks

AI is not just an add-on; it must be at the core of future telecom networks. Manish highlighted several ways AI can drive growth:

• Automation of network operations: AI-driven predictive maintenance and self-optimising networks reduce downtime and operational expenses.
• Enhanced service delivery: AI enables hyper-personalised customer experiences and intelligent traffic management.
• Operational efficiency: AI optimises energy consumption, spectrum allocation, and overall network resource utilisation.

Manish emphasised that AI-native networks will be a defining feature of 6G, making networks more autonomous, efficient, and scalable.

Cloud-native Architectures: The Foundation for Scalability

Moving beyond traditional, hardware-centric networks is essential. Manish advocates for a cloud-first approach, where telecom networks are:

• Software-defined and virtualised, reducing dependence on costly proprietary hardware.
• Highly scalable, allowing operators to adjust capacity dynamically.
• Interoperable and open, fostering innovation through Open RAN and disaggregated networks.

By embracing cloud-native principles, telcos can accelerate service delivery, reduce costs, and stay competitive in an increasingly software-driven ecosystem.

AI Infrastructure: Scaling from Edge to Core

A key enabler of AI and cloud-native networks is the AI Factory approach, which provides scalable infrastructure from mega-scale data centres to the edge. Manish highlighted how AI workloads must be supported across different network layers—from on-premise enterprise deployments to far-edge, near-edge, and core data centres.

Dell Technologies' AI Factory is designed to:

• Support diverse AI edge use cases in telecom.
• Handle power and cooling constraints, crucial for efficient AI model training and inference.
• Leverage cloud-native architectures to ensure seamless scalability and automation across the entire network.

This modular infrastructure ensures that telecom networks can efficiently process AI workloads at every layer, enabling real-time decision-making and optimised operations.

Overcoming Challenges in AI and Cloud Adoption

Despite the clear benefits, Manish acknowledged key barriers:

• Legacy infrastructure: Transitioning from traditional networks requires significant investment.
• Security and privacy concerns: AI-driven automation raises questions about data integrity and network security.
• Industry mindset shift: Operators must adopt a culture of innovation and rapid iteration.

Addressing these challenges requires industry-wide collaboration, strong partnerships with cloud providers, and a commitment to open innovation.

Conclusion: The Time to Act is Now

Manish’s message to the industry was clear—AI and cloud are not future aspirations; they are essential for telecom survival and growth. By leveraging AI-native automation and cloud-native architectures, operators can reignite growth, drive efficiency, and prepare for the 6G era.

Watch Manish Singh’s full keynote embedded below:

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Beyond KPIs: The Role of Key Value Indicators (KVIs) in 6G

Key Performance Indicators (KPIs) have long been the primary benchmarks for evaluating mobile network performance. However, as 6G moves towards a broader societal and environmental impact, the European Hexa-X-II project and its partners advocate for integrating Key Values (KVs) and Key Value Indicators (KVIs) into network design and evaluation.

On 18 December 2024, Hexa-X-II hosted an insightful webinar to highlight the significance of KVIs in shaping the future of 6G. The session underscored how KVIs can complement KPIs by assessing technology’s impact on sustainability, digital inclusion, trust, and ethical considerations. 

While the video hasn't been shared, you can download the slides from here.

Why KVIs Matter in 6G Development

Traditionally, mobile network development has been driven by KPIs—throughput, latency, reliability, and spectrum efficiency. However, as 6G aims to support broader global goals such as the UN Sustainable Development Goals (SDGs), a shift towards value-based design is necessary. Hexa-X-II proposes a structured methodology where:

• Human and Planetary Goals are identified (e.g., sustainability, digital inclusion, trust).
• Key Values (KVs) are derived from these goals to reflect technology’s intended benefits and potential risks.
• Key Value Indicators (KVIs) provide qualitative or quantitative measures to assess whether these values are met.

From Theory to Application: The Hexa-X-II Process

One of the core challenges in applying KVIs is the interdisciplinary nature of the assessment. Unlike KPIs, which are primarily technical, KVIs require social, economic, and environmental considerations. The Hexa-X-II approach includes:

• Defining Use Case KVIs, which assess the impact of a specific application.
• Defining Enabler KVIs, which measure how well a technical enabler (e.g., AI, NTN, RIS) contributes to key values.
• Mapping KVIs to existing KPIs where possible and identifying gaps where new indicators are needed.
• Iteratively refining KVIs based on real-world evaluations.

A Case Study: Cooperating Mobile Robots

One example discussed in the webinar was the use of cooperating mobile robots, a use case that benefits from 6G-enabled ultra-reliable low-latency communication (URLLC). While KPIs can measure performance (e.g., latency, reliability), KVIs help evaluate the broader impact, such as:

• Environmental KVIs: Energy efficiency, material usage, electronic waste reduction.
• Social KVIs: Job displacement vs. job creation, worker safety, accessibility.
• Economic KVIs: Business viability, affordability, and risk of monopolisation.

By systematically assessing these factors, the Hexa-X-II framework ensures that 6G technology is not just high-performing but also aligned with societal needs.

Lessons Learned and Future Outlook

The adoption of KVIs presents several challenges, including subjective assessments, measurement difficulties, and the need for multi-stakeholder collaboration. However, Hexa-X-II emphasises that:

• Technology impact should be continuously monitored using KVIs.
• Qualitative and quantitative assessments must be combined, rather than relying solely on measurable KPIs.
• A system-level approach is required, integrating perspectives from sustainability, business, and social sciences.

As 6G research advances, KVIs will play a crucial role in ensuring that next-generation networks contribute meaningfully to global sustainability and inclusivity. The Hexa-X-II initiative provides a foundational methodology for integrating values into the traditionally KPI-driven telecom landscape — an approach that could redefine how we measure success in the 6G era.

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Lessons from ANRW ’24: AI and Cloud in 5G/6G Systems

The ACM, IRTF & ISOC Applied Networking Research Workshops (ANRW) offer a vibrant forum for researchers, vendors, network operators, and the Internet standards community to exchange emerging results in applied networking research. To foster collaboration across these diverse groups, ANRW events are co-located with IETF standards meetings, typically held annually in July. These workshops prioritise interactive discussions and engagement, complementing traditional paper presentations.

ANRW '24, held on 23 July 2024 at the Hyatt Regency Vancouver, brought together industry leaders and academics to share insights on advancing networking technologies. Among the standout sessions was a keynote presentation by Sharad Agarwal, Senior Principal Researcher at Microsoft. His keynote titled, "Lessons I Learned in Leveraging AI+ML for 5G/6G Systems", highlighted pivotal themes influencing telecom and networking.

Sharad distilled his experiences into three key lessons, each underscored by examples of research and systems developed to address specific challenges in the telecom industry:

1. Leverage Cloud Scale to Overcome Limitations of Deployed Protocols: He emphasised that the scale of cloud computing is critical to managing the massive demands of modern telecom networks. For instance, systems like TIPSY (Traffic Ingress Prediction SYstem) demonstrate how AI and ML can predict traffic ingress points across thousands of peering links, helping to avoid bottlenecks and ensure optimal traffic distribution.
2. Custom Learning Algorithms vs. Off-the-Shelf Solutions: While bespoke algorithms offer higher precision for niche applications, their complexity and deployment challenges often outweigh their benefits. Sharad argued for balancing innovation with practicality, advocating for leveraging pre-built AI and ML models wherever possible to streamline integration.
3. Mitigate Risks of AI Hallucinations through Careful System Design: Acknowledging the risks posed by unreliable AI outputs, he stressed the importance of robust system design. Using LLexus, an AI-driven incident management system, as an example, Sharad highlighted techniques like iterative plan generation, validation rules, and human auditing as essential safeguards against AI errors.

The talk also delved into broader trends shaping the telecom landscape:

• Cloudification of Telecom Infrastructure: The shift from hardware-based to software-based network functions, underpinned by cloud-native principles, has revolutionised telco infrastructure. This transformation facilitates rapid upgrades, reduces costs, and introduces new opportunities for AI-driven analytics.
• Challenges in Performance and Reliability: Ensuring high throughput, low latency, and carrier-grade reliability in cloudified networks remains a significant hurdle. Innovations like PAINTER and LLexus demonstrate how AI and ML are being applied to optimise these aspects.
• Emerging Business Models and Private Deployments: The integration of new radio technologies and virtualised network functions is driving novel revenue streams, such as private 5G/6G networks for mission-critical applications like factory automation.

Finally, Sharad’s keynote underscored how AI, ML, and cloud computing are reshaping the telecom industry, particularly in the era of 5G and the forthcoming 6G. By leveraging the scale of cloud infrastructure, balancing algorithmic complexity, and designing systems with resilience against AI pitfalls, the industry is poised to meet its ambitious goals of high bandwidth, low latency, and unparalleled reliability.

The video of his talk is embedded below and the slides are available here:

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