A DT is a virtual representation of a real-world physical system, process, or object continuously collecting and using real-time data to update itself. It thereby keeps track of the state and behaviour of the original target, enabling better monitoring, analysis, simulation, and optimisation without interfering with the actual physical asset or system.
Through sensors and data integration (and increasingly AI techniques), DTs can predict maintenance needs, test the outcomes of different scenarios or possible actions, and improve asset/system performance, while reducing risks and costs.
Typical use cases for DTs include representing individual assets like factories, vehicles, or machines, as well as larger systems such as smart cities or environmental zones like watersheds and forests.
The same broad principles can be applied to “hard” assets such as machines, or less tangible but still real-world phenomena such as the atmosphere, weather systems, electricity flows, or radio spectrum. Creating DTs for these assets still requires sensors, data integration, modelling, and appropriate computational platforms.
Where an industrial digital twin might focus on preventative maintenance and asset performance optimisation, a spectrum digital twin might examine how to mitigate the risks of interference or optimise the effective utilisation of a given frequency band. It could operate at a local level (for instance, inside a building or enterprise campus), across a mid-sized area such as an airport or city, or potentially across an entire nation and beyond.
A significant difference to consider is that a spectrum DT will likely have to deal with multiple separate frequency bands occupying the same physical space, with the potential for interference between them.
While wireless companies regularly use design, planning, and monitoring tools, they are often static or updated irregularly. A mobile network operator (MNO) might use periodic drive tests to check their network coverage and performance, as well as data from their network elements and end-user device feedback. However, this does not mean they have a proper “twin” or realistic model of the spectrum environment. Regulators and other industry participants are similarly limited in their views of the “spectrum-scape”.
A spectrum-specific DT would typically combine data from radio transmitters with propagation maps generated from measurements and models. This information is then overlaid onto geographical information systems (GIS) for large-scale environments or building information models (BIM) for smaller-scale or indoor settings. It integrates a real-time snapshot of sensed and reported radio activity with a historical log of past activities, providing a comprehensive view of spectrum usage.
Improve existing operations of spectrum management or a specific network
Maintain and improve its own realistic depiction of the “spectrum-scape”
Enable potential new capabilities and services, based on the DT’s knowledge and data
Real-time sensing and data collection mechanisms, with a high degree of distribution
Various specialised spectrum DTs (or overlays) will likely evolve to meet the needs of different stakeholders such as regulators, MNOs, enterprises, and government / military users.
There are many potential use cases for a spectrum DT, depending on its accessibility and granularity. These could include:
Multiple technology enablers are required for creating spectrum digital twins, but two especially critical ones are sensing abilities and various AI techniques for analysis, modelling, simulation, and prediction.
There are numerous sources of information about the spectrum environment. Still, ideally, a DT will collect data from “independent” sensors beyond the self-reporting and RF measurement from expected / registered radio systems such as cellular base stations and users’ handsets. Regulatory authorities and other spectrum management agencies will be particularly interested in operating their own sensing systems rather than relying on licensees’ reported data—which, in any case, may not be realistic for legacy or secure systems.
In essence, there needs to be a more real-time and distributed equivalent of drive tests and other periodic testing, especially if the DT is designed to be multi-technology and multi-band. Attention also needs to be paid to the patterns of interference and propagation and how those change with time or other situational conditions. That allows the twin to be updated directly from sensed data and with modelling where needed, making detecting anomalies easier.
There is also likely to be a strong link with the evolution of software-defined radios (SDRs) and other flexible, programmable platforms such as Open RAN and Open Wi-Fi that can repurpose normal wireless transmission and reception as a sensing function.
Depending on the desired use cases for the DT, spectrum sensors may be concentrated in particular geographic zones, such as maritime areas, airports, near borders, around urban centres, or military bases or deployment regions. They may be collocated with existing wireless assets such as broadcasting infrastructure or cell towers or linked to deployments of other environmental sensing and monitoring systems.
AI and Machine Learning will also be critical for DTs, alongside sensing. Advanced algorithms can process sensing data to identify patterns, predict potential issues, and endogenously improve the accuracy of spectrum usage models. Other AI functions include:
It should be noted that creating and operating spectrum DTs has numerous challenges, which will need to be addressed with iterative research, testing, and deployment cycles. This emergent domain will likely proceed in multiple steps and phases. Issues to address include:
As well as direct technical challenges, there are additional considerations around standardisation, regulation, and commercialisation. In particular, there are potential risks and threat surfaces to any form of spectrum monitoring or modelling, especially where some of the user groups are related to critical infrastructure or emergency / defence user groups.
While spectrum-specific DTs are certainly an interesting future trend, there is currently limited real-world deployment (at least that which is publicly acknowledged). While there are numerous research projects in this space, it seems likely that full deployment will only occur over the next 5–10 years in tandem with the evolution of adjacent technologies such as 6G, radio-centric AI tools, and better systems of distributed sensing and data collection.
There are three vectors to watch here:
Evolution and deployment of key technical enablers, such as sensing grids and spectrum-optimised AI models
Growth of key DT customer groups and use cases, such as new dynamic spectrum-sharing models, military spectrum sophistication, and new AI-native 6G standards capable of using DT-derived data and simulations
Regulatory support for DTs, both in terms of internal need for advanced spectrum management and in addressing privacy and security implications for other users.
Also, it is currently difficult to gauge whether spectrum DTs will operate primarily on a standalone basis or if they will be integrated into other “system” DTs.
For instance, MNOs will likely run digital twins of their entire networks, including both the active radio systems and spectrum, as well as the hard infrastructure components such as cell-towers, backhaul, and indoor wireless systems. They will want to optimise their investments in spectrum alongside physical assets such as rented rooftops, power supplies, and fibre connectivity. However, they will have limited data about other spectrum users’ performance and interference either caused or experienced.
Conversely, a dedicated spectrum management agency within government will probably want a more multi-band, multi-technology “pure” DT, but may lack some of the input data from the network infrastructure itself. Over time, we can probably expect spectrum DTs to develop a variety of interfaces and integration options.
In summary, we should expect a deepening of interest, investment and utility from spectrum DTs. Potentially, they could form a central part of future spectrum management, as enablers for regulators to better manage demand for spectrum and optimise radio frequency utilisation and value, while minimising interference risk in real-time. As part of that, we should also strive for cross-sector collaboration, in order to integrate data and models from multiple technologies and spectrum stakeholder groups.
Portable and rugged high-performance RF sensor for real-time 24/7 spectrum monitoring and geolocation of transmitters
Dean Bubley, founder of Disruptive Analysis, writes guest posts for CRFS. He is an independent analyst and advisor to the wireless and telecoms industry and has covered the evolution of private cellular networks since 2001.