The high-fidelity digital twins will help telcos optimize tower placement, manage networks, and build stronger, more stable telecom grids.
HEAVY.ai and NVIDIA will expand their collaboration to include additional partners, Bain & Company and Maxar. This will provide high-fidelity digital twins for the telecom industry. Companies will have access to data on a global scale, expanding what is possible using cutting-edge technology.
The new collaboration leverages NVIDIA Omniverse, Maxar’s high-resolution Precision3D Data (built from satellite imagery), and the full domain knowledge of Bain. Together with HeavyRF’s compute power, telcos will have reliable, quality data to inform digital twin models in a fraction of the time.
The resultant high-fidelity digital twins can be very beneficial for telecom networks in several ways:
- Predictive Maintenance: High-fidelity digital twins can simulate the behavior of physical network assets in real time. Network operators can predict potential equipment failures and take preventive measures by analyzing data collected from these simulations. This can help reduce downtime and maintenance costs.
- Network Optimization: Digital twins can be used to optimize network performance and identify areas for improvement. Network operators can identify bottlenecks, optimize network traffic, and improve overall performance by analyzing data from sensors and other sources.
- Faster Troubleshooting: High-fidelity digital twins can help network operators quickly identify and troubleshoot network issues. By simulating the network and analyzing data in real time, operators can quickly identify the root cause of a problem and take appropriate action to resolve it.
- Virtual Testing: Digital twins can simulate and test new network configurations, upgrades, and deployments. This can help network operators test new technologies and configurations in a virtual environment, reducing the risk of costly mistakes in the real world.
The existing collaboration between HEAVY.ai and NVIDIA gives telcos solutions like HeavyRF, a solution built on the NVIDIA Omniverse platform. Bain and Maxar’s addition will expand capabilities and provide more accurate and detailed digital twins faster. Telcos are expected to soon enjoy up-to-the-minute updates on all digital twins from a single screen.
See also: 5G and Digital Twins: It Takes an Ecosystem
What are current challenges for telcos when building digital twins?
Developing high-fidelity digital twins for telecom networks can be challenging due to several factors:
- Data Integration: High-fidelity digital twins require large amounts of data from different sources, such as network equipment, sensors, and customer usage patterns. Integrating this data can be challenging, as it may come from different formats, sources, and vendors.
- Data Quality: The accuracy and completeness of the data used to develop digital twins is critical to their success. However, data quality can be challenging to ensure, as it may be affected by sensor accuracy, data loss, and human error.
- Model Complexity: Developing high-fidelity digital twins can be complex, as it requires the creation of accurate and detailed models of network components and their behavior. These models may be challenging to develop and require specialized knowledge and expertise.
- Scalability: Telecom networks are complex and large, and developing digital twins that can simulate the behavior of the entire network can be challenging. Ensuring that digital twins can scale to handle the complexity and size of the network is critical to their success.
- Security: Digital twins require access to sensitive network data, which hackers or cybercriminals may target. Ensuring the security of digital twin data and protecting it from cyber threats is critical to their success.
Developing high-fidelity digital twins for telecom networks requires significant expertise, resources, and attention to detail to overcome these challenges and ensure success. The collaboration revolutionizes access to both historical and real-time data so that telcos can accelerate business decisions without worrying about model complexity or integration challenges.
How can the telecom industry overcome these challenges?
Overcoming the challenges of developing high-fidelity digital twins for telecom networks requires technical expertise, collaboration, and a commitment to data quality and security. Telecoms can benefit from partnering with industry experts and technology vendors to access specialized skills, tools, and solutions to develop successful digital twin implementations. Ultimately, it will require several strategies to overcome the challenges of developing high-fidelity digital twins:
Data Integration
Telecoms can use data integration platforms and tools to consolidate data from different sources into a unified format. Data quality checks can be performed to ensure the accuracy and completeness of the data. Telcos will be able to manage all digital twins from a single screen, helping reduce the opacity of big data inputs.
Data Quality
Telecoms can use data cleansing and data profiling tools to improve data quality. Data governance practices can be implemented to ensure data accuracy and completeness, and data lineage and auditing can be used to track changes to data over time. The new collaboration will improve resolution, accuracy, and speed of the data collection and analysis process to help ensure telcos can trust the insights coming from digital twin models.
Model Complexity
Collaborating with external partners and industry experts can also help build the necessary expertise. Telcos will have the advantage of working with four domain experts to develop and launch truly remarkable digital twins and create accurate and detailed models of network components and their behavior. And by leveraging Maxar’s high-resolution 50-centimeter clutter height data, HeavyRF empowers customers to interactively model signal propagation at a regional, national, or global level. This provides unparalleled opportunities for telecommunications planning on a global scale.
Scalability
Telecoms can use cloud computing and distributed computing technologies to scale digital twins to handle the complexity and size of the network. Parallel processing and big data technologies can also handle the large volumes of data involved in digital twin simulations. Previous solutions were slow to process 3D geodata, but the collaboration enables tools that can leverage this data faster and more efficiently. Companies will be able to ensure service stability for years to come.
Security
Telecoms can implement robust security practices, such as encryption and access controls, to protect digital twin data from cyber threats. Regular security assessments and vulnerability testing can also help identify and address potential security issues.
The collaboration will enable stronger decision-making for the modern age
Telecoms can now analyze multiple layers of data at once, thanks to HeavyRF. The collaboration empowers users to understand the full context of the simulated environment—location and material composition of obstructions, as well as customer locations and activity included—to make the most informed and profitable operational decisions.
These tools will optimize tower placement, manage networks, and build stronger, more stable telecom grids by allowing telcos to simulate environments easily with each new variable. It’s going to make planning more accurate and increase long-term profitability. Ultimately, it will enable real-time, dynamic use of big data, revolutionizing decision-making.
