Telco digital twins: Cool tech or real value?

Definition of a digital twin

Digital twin is a familiar term with a well-known definition in industrial settings. However, in a telco setting it is useful to define what it is and how it differs from a standard piece of modelling. This research discusses the definition of a digital twin and concludes with a detailed taxonomy.

An archetypical digital twin:

  • models a single entity/system (for example, a cell site).
  • creates a digital representation of this entity/system, which can be either a physical object, process, organisation, person or abstraction (details of the cell-site topology or the part numbers of components that make up the site).
  • has exactly one twin per thing (each cell site can be modelled separately).
  • updates (either continuously, intermittently or as needed) to mirror the current state of this thing. For example, the cell sitescurrent performance given customer behavior.

In addition:

  • multiple digital twins can be aggregated to form a composite view (the impact of network changes on cell sitesin an area).
  • the data coming into the digital twin can drive various types of analytics (typically digital simulations and models) within the twin itself – or could transit from one or multiple digital twins to a third-party application (for example, capacity management analytics).
  • the resulting analysis has a range of immediate uses, such as feeding into downstream actuators, or it can be stored for future use, for instance mimicking scenarios for testingwithout affecting any live applications.
  • a digital twin is directly linked to the original, which means it can enable a two-way interaction. Not only can a twin allow others to read its own data, but it can transmit questions or commands back to the original asset.

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What is the purpose of a digital twin?

This research uses the phrase “archetypical twin” to describe the most mature twin category, which can be found in manufacturing, operations, construction, maintenance and operating environments. These have been around in different levels of sophistication for the last 10 years or so and are expected to be widely available and mature in the next five years. Their main purpose is to act as a proxy for an asset, so that applications wanting data about the asset can connect directly to the digital twin rather than having to connect directly with the asset. In these environments, digital twins tend to be deployed for expensive and complex equipment which needs to operate efficiently and without significant down time. For example, jet engines or other complex equipment. In the telco, the most immediate use case for an archetypical twin is to model the cell tower and associated Radio Access Network (RAN) electronics and supporting equipment.

The adoption of digital twins should be seen as an evolution from today’s AI models

digital-twins-evolution-of-todays-ai-models-stl-partners

*See report for detailed graphic.

Source: STL Partners

 

At the other end of the maturity curve from the archetypical twin, is the “digital twin of the organisation” (DTO). This is a virtual model of a department, business unit, organisation or whole enterprise that management can use to support specific financial or other decision-making processes. It uses the same design pattern and thinking of a twin of a physical object but brings in a variety of operational or contextual data to model a “non-physical” thing. In interviews for this research, the consensus was that these were not an initial priority for telcos and, indeed, conceptually it was not totally clear whether the benefits make them a must-have for telcos in the mid-term either.

As the telecoms industry is still in the exploratory and trial phase with digital twins, there are a series of initial deployments which, when looked at, raise a somewhat semantic question about whether a digital representation of an asset (for example, a network function) or a system (for example, a core network) is really a digital twin or actually just an organic development of AI models that have been used in telcos for some time. Referring to this as the “digital twin/model” continuum, the graphic above shows the characteristics of an archetypical twin compared to that of a typical model.

The most important takeaway from this graphic are the factors on the right-hand side that make a digital twin potentially much more complex and resource hungry than a model. How important it is to distinguish an archetypical twin from a hybrid digital twin/model may come down to “marketing creep”, where deployments tend to get described as digital twins whether they exhibit many of the features of the archtypical twin or not. This creep will be exacerbated by telcos’ needs, which are not primarily focused on emulating physical assets such as engines or robots but on monitoring complex processes (for example, networks), which have individual assets (for example, network functions, physical equipment) that may not need as much detailed monitoring as individual components in an airplane engine. As a result, the telecoms industry could deploy digital twin/models far more extensively than full digital twins.

Table of contents

  • Executive Summary
    • Choosing where to start
    • Complexity: The biggest short-term barrier
    • Building an early-days digital twin portfolio
  • Introduction
    • Definition of a digital twin
    • What is the purpose of a digital twin?
    • A digital twin taxonomy
  • Planning a digital twin deployment
    • Network testing
    • Radio and network planning
    • Cell site management
    • KPIs for network management
    • Fraud prediction
    • Product catalogue
    • Digital twins within partner ecosystems
    • Digital twins of services
    • Data for customer digital twins
    • Customer experience messaging
    • Vertical-specific digital twins
  • Drivers and barriers to uptake of digital twins
    • Drivers
    • Barriers
  • Conclusion: Creating a digital twin strategy
    • Immediate strategy for day 1 deployment
    • Long-term strategy

Related research

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6G: Hype versus reality

What is 6G and why does it matter?

Who’s driving the 6G discussion?

There already are numerous 6G visions, suggested use-cases and proposed technical elements. Many reflect vendors’ or universities’ existing specialist research domains or IPR in wireless, or look to entrench and extend existing commercial models and “locked-in” legacy technology stacks.

Others start from broad visions of UN development goals and policymakers’ desires for connected societies, and try to use these to frame and underpin 6G targets, even if the reality is that they will often be delivered by 5G, fibre or other technologies.

The stakeholder groups involved in creating 6G are wider than for 5G – governments, cloud hyperscalers / tech-co’s, industrial specialists, NGOs and many other groups seem more prominent than in the past, when the main drivers came from MNOs, large vendors and key academic clusters.

Over time, a process of iteration and “triangulation” will occur for 6G, initially starting with a wide funnel of ideas, which are now starting to coalesce into common requirements – and then to specific standards and underlying technical innovations. By around 2024-25 there should be more clarity, but at present there are still many directions that 6G could take.

What are they saying?

Discussions with and available material from parties interested in 6G discusses a wide range of new technologies (e.g. ultra-massive MIMO) and design goals (e.g. speeds of 1Tbps). These can be organised into six categories to provide a high-level set of futuristic statements that underpin the concept of 6G,  as articulated by the various 6G consortia and governing bodies:

  1. Provision of ultra-high data rate and ultra-low latency: Provision of up to 1Tbps speeds and as low as 1 microsecond latency – both outdoors and – implicitly at least – indoors.
  2. Use of new frequencies and interconnection of new network types: Efficient use of high, medium, and low-frequency bands, potentially including visible light and >100GHz and even THz spectrum. This will include possible coordination between non-terrestrial networks and other existing networks, and new types of radio and antenna to provide ubiquitous coverage in a dispersed “fabric” concept, rather than traditional discrete “cells”.
  3. Ultra-massive MIMO and ultra-flexible physical and control layers: The combination of ultra-large antenna arrays, intelligent surfaces, AI and new sensing technologies working in a range of frequency bands. This will depend on the deployment of a range of new technologies in the physical and control layers to increase coverage and speed, while reducing cost and power consumption.
  4. High resolution location: The ability to improve locational accuracy, potentially to centimetre-level resolutions, as well as the ability to find and describe objects in 3D orientation.
  5. Improved sensing capabilities: Ability to use 6Gradio signals for direct sensing applications such as radar, as well as for communications.
  6. General network concepts: A variety of topics including the concept of a distributed network architecture and a “network of networks” to improve network performance and coverage. This also includes more conceptual topics such as micro-networks and computing aware networks. Finally, there is discussion on tailoring 6Gfor use of / deployment by other industries beyond traditional telcos (“verticals”), such as enhancements for sectors including rail, broadcast, agriculture, utilities, among others, which may require specific features for coverage, sector-specific protocols or legacy interoperability.

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How is 6G different to 5G?

In reality, the boundaries between later versions of 5G and 6G are likely to be blurred, both in terms of the technology standards development and in the ways marketers present network products and services. As with 5G, the development of 6G will take time to reach many of the goals above. From 3GPP Release 18 onwards, 5G is officially being renamed as “5G Advanced”, mirroring a similar move in the later stages of 4G/LTE development. Rel18 standards are expected to be completed around the end of 2023, with preliminary Rel19 studies also currently underway. Rel20 and Rel21 will continue the evolution.

Figure 1: Roadmap for 6G

Source: Slides presented by Bharat B Bhatia President, ITU-APT Foundation of India at WWRF Huddle 2022

However, from 2024 onwards, the work done at 3GPP meetings and in its various groups will gradually shift from enhancing 5G to starting the groundwork for 6G – initially defining requirements in 2024-25, then creating “study items” in 2025-26. During that time, new additions to 5G in Rel20/21/22 will get progressively thinner as resources are devoted to 6G preparations.

The heavy lifting efforts on “work items” for 6G will probably start around 2026-27, with 5G Advanced output then dwindling to small enhancements or maintenance releases. It is still unclear what will get included in 5G Advanced, versus held over until 6G, but the main emphasis for 6G is likely to be on:

  • Greater performance and efficiency for mobile broadband, with attention paid to MIMO techniques, better uplink mechanisms and improved cell-to-cell handover
  • Additional features for specific verticals, as well as V2X deployments and IoT
  • Support of new spectrum bands
  • Improvements in mapping and positioning
  • Enhanced coverage and backhaul, for instance by establishing “daisy-chains” of cell sites and extensions and repeaters, including using 5Gfor backhaul and access
  • More intelligence and automation in the 5Gnetwork core, including improvements to slicing and orchestration
  • Better integration of non-terrestrial networks, typically using satellites or high-altitude platforms
  • Capabilities specifically aimed at AR/VR/XR
  • Direct device-to-device connections (also called “sidelink”) that allow communication without the need to go via a cell tower.

We can expect these 5G Advanced areas to also progress from requirements, to study, and then to work items during the period from 2022-27.

However, these features will mostly be an evolution of 5G, rather than a revolution by 5G. While there may be a few early moves on areas such as wireless sensing, Releases 18-21 are unlikely to include any radical breakthroughs. The topics we discuss elsewhere in this report, such as potential use of terahertz bands, blending of O-RAN principles of disaggregation, and new technology domains such as smart surfaces, will be solidly in the 6G era.

An important point here is that the official ITU standard for next-gen wireless, likely to be called IMT2030, is not the same as 3GPP’s branding of the cellular “generation”, or individual MNOs service names. There may well be early versions of 6G cellular, driven by market demand, that don’t quite match up to the ITU requirements. Ultimately 3GPP is an industry-led organisation, so may follow the path of expediency if there are urgent commercial opportunities or challenges.

In addition, based on the experience of 4G and 5G launches, it is probable that at least one MNO will try to call a 5G Advanced launch “6G” in their marketing. AT&T caused huge controversy – and even lawsuits – by calling a late version of LTE “5Ge” (5G evolution), even including the icons on some phones’ screens, while Verizon’s early 5G FWA systems were actually a proprietary pre-standard version of the technology.

If you’re a purist about these things – as we are – prepare to be howling in frustration around 2027-28 and describing new services as “fake 6G”.

Table of Contents

  • Executive Summary
    • What is 6G?
    • Key considerations for telcos and vendors around 6G
    • What should telcos and vendors do now?
    • 6G capabilities: Short-term focus areas
    • Other influencing factors
  • What is 6G and why does it matter?
    • Who’s driving the 6G discussion?
    • What are they saying?
    • The reality of moving from 5G Advanced to 6G
    • Likely roll out of 6G capabilities
  • Regulation and geopolitics
    • The expected impact of regulation and geopolitics
    • Summary of 6G consortiums and other interested parties
  • 6G products and services
  • Requirements for 6G
    • AI/ML in 6G
    • 6G security
    • 6G privacy
    • 6G sustainability
  • Drivers and barriers to 6G deployment
    • Short-term drivers
    • Short-term barriers
    • Long-term drivers
    • Long-term barriers
  • Conclusion: Realistic expectations for 6G
    • The reality: What we know for certain about 6G / IMT2030
    • Possibilities: Focus areas for 6G development
    • The hype: Highly unlikely or impossible by 2030

Related research

 

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Four goals for the data-driven telco

Becoming a data-driven telco

There have been many case studies over the last five years demonstrating the disruption caused by “data-driven businesses”, i.e. those using insights to understand customers, automate processes, change their business models and drive new revenues. In the future, this concept will become an integral part of what it takes to compete successfully, allowing organisations to understand and run all parts of their operations, work with their customers and partners and take part in external activities in new ecosystems. This applies to telecoms operators as much as any other industry.

This research builds on a range of reports STL Partners has previously published on strategic topics related to telcos’ use of data, including:

This research turns to the practical topics of delivering on these strategic goals. The diagram below offers an overview of the drivers and barriers affecting delivery areas such as telco data management and machine learning (ML) in the short and longer term.

Drivers and barriers to being a data-driven telco

Source: STL Partners

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What capabilities should telcos develop?

Telcos are reasonably sophisticated users of data, but their particularly complex web of legacy systems requires a good deal of work around data management and governance to enable the extraction of data sets to give 360-degree view of the customer – and increasingly to provide training data for algorithms.

In the mid-term, telcos that are successful in selling IoT and becoming ecosystem players will require new A3 to deal with the increasing number of services, devices, price points and parties involved in providing service to a customer. Our research suggests that there is a range of new A3 technologies that can provide the automation and intelligence for this, as well as for the underlying data management processes.

In the longer-term, A3 will speed up decision making, impacting company strategy, new product and service creation, and customer experience. Humans will increasingly be supported by AI-, ML- and automation-powered tools in their decision-making. A similar progression will occur among competitors in telecoms, and in adjacent markets, increasing the complexity and speed of doing business. Besides integrating A3 into human workflows, working at increasing speed will depend on getting richer insights out of the available data with techniques such as small data and creation of synthetic data.

Capabilities for a data-driven telco

Source: STL Partners

 

Table of contents

  • Executive Summary
    • Capabilities telcos should develop over the medium term
    • What will telcos focus on in the mid-term?
    • Next steps
  • Becoming a data-driven telco
    • Short term drivers
    • Barriers in the short term
    • Long term drivers
    • Barriers in the long term
  • Availability of data
    • Use of data fabrics
    • Better data labelling
    • Rise of synthetic data
    • More intelligent data selection
    • Telco strategies for cloud usage
  • Equipping people
    • Augmented analytics and business intelligence
    • Decision intelligence
  • Work on governance
    • Governance across the telco
    • Agility in governance
    • Governance for AI and machine learning
    • Ethical governance
    • Improved measurement of governance
    • Governance in ecosystems
  • Index