The telecommunications industry is on the verge of transformation, driven by the emergence of AI-native digital twins and open-source simulation tools that Nvidia released during the GTC 2025 conference.
Nvidia announced the Sionna 1.0 release, a technology that signalled a new era for wireless network research, planning, and operations. NVIDIA’s Sionna represents a breakthrough because of its
- Open-source nature: Sionna will provide access to advanced simulation and planning capabilities, which will enable startups and smaller players to innovate rapidly.
- Full-stack differentiable design: For the first time, Sionna enables AI-driven optimization of entire communication systems, not just isolated components
However, as much as the tool is open source, so should the data. That is why we at Databricks are so excited to explore this tool. Integrating Databricks’ capabilities with NVIDIA Sionna creates a robust framework for AI-driven network optimization. Databricks provides sets of opensource tools and features that can help you truly democratize your telecom digital twin;
In the next series of articles, we’ll implement specific use cases of Sionna on Databricks, such as AI-optimized beamforming and predictive tower maintenance, demonstrating how these tools can help you solve real-world telecom challenges. Telecommunications infrastructure is complex; it consists of physical and digital networks allowing voice, data, and video to be transmitted between users and systems. Many readers outside the telecom industry may not be familiar with how these networks are structured or how their components interact, so let’s have a quick telecom 101, and oversimplify the complicated infrastructure that makes real-time connectivity possible.
Telecom 101
In this section, I try to explain a telecommunication network from a helicopter view, enough to understand some of the datasets it generates and how it can be used to improve network performance. It necessitates some oversimplification to avoid complexities.
A telecommunications infrastructure is a set of systems and devices necessary for exchanging information between users connected to the network. It uses electronic means to transmit information, typically through cables, radio waves, or other communication technologies. At a high level, a telecom network has 3 main components:
- Radio Access Network (RAN): This network connects end-user devices (phones, IoT devices) to the network via base stations, antennas, and small cells. It is responsible for the wireless interface and is the most visible part of the network to the public.
- Transmission Network: Acts as the backbone, carrying data between the RAN, the core network, and external networks. It uses technologies like fiber optic cables, microwave links, and sometimes satellite links for connectivity.
- Core Network: The central part of the system handles call routing, data switching, authentication, billing, and connectivity to external networks (like the internet). It includes mobile switching centers, packet gateways, and subscriber databases.
Data from user devices is transmitted via the RAN to the core network, often traversing the transmission network. The core network manages and routes this data, ensuring it reaches its destination, whether that’s another phone, a website, or a cloud service.
Data centers, network management systems, and various types of telecommunications equipment (routers, switches, modems) are also critical to the functioning of the network that we are skipping them in this introduction.
This image is a back-of-napkin illustration of a complex telecommunication network focusing on antenna-to-user communication. Typically, each telecom tower has multiple Radio Access Network (RAN) antennas that support one or many cells (for simplification, we are going to use cell and antenna interchangeably, meaning each antenna has one cell). Cells are located on base stations in a location, height, elevation, and orientation that ensures the best coverage for users. For instance, if each cell covers 120 degrees, we need three cells to cover the environment. If there was no imperfection, blockage or interference, we would have antenna patterns like the image below. The idea is that the base stations serve cells and those cells give you coverage everywhere.
Even though a simple omnidirectional cell generates a circle coverage pattern, hexagons are commonly used to represent cell coverage in cellular networks as they provide efficient coverage, which helps to minimize interference between adjacent cells and allows for a more consistent distance between neighbouring cells. Many algorithms used for optimization and analysis in cellular networks can be more easily applied to hexagonal grids due to their regularity and symmetry (there are many books and resources to learn more about wireless cellular design that when you want to go deep can help you understand the logic such as Molisch, Andreas F. Wireless communications. Vol. 34. John Wiley & Sons, 2012.) .
In summary, hexagons are used in cellular network design because they provide a practical, efficient, and mathematically manageable way to model and optimize coverage areas. You can find Canada’s hexagon Grid here, which has been used by Innovation Science and Economic Development Canada for Spectrum licensing and management activities
RAN optimization
RAN optimization gets complicated very quickly The coverage map of RAN cells is crucial for radio frequency (RF) planning engineers. Coverage maps help RF engineers to asses user connectivity issues. Antenna performance is measured using key performance indicators (KPIs) such as throughput (transmission rate), availability, accessibility, retainability, and latency (of signal), received signal strength (RSS), and signal to noise and interference Ratio (SINR).
Antenna configuration parameters influence these KPIs, including location, height, power, frequency, bandwidth, antenna orientation (pitch, yaw, roll), polarization, and array design, specifically UniForm planar array (UPA). Besides the antenna configuration surrounding buildings and signal sources, weather situation hardware impairment and user distribution would also affect the KPI service. Some of these factors can be measured and simulated, especially with the help of Sionna we can import landscape and create a digital twin of location. Some other aspects are not static (such as user location), and for simulation, we have to use probability distribution.
Network maintenance process
The current telecom troubleshooting process (generally) begins when either a customer complaint or network KPI degradation is reported. Customer care first attempts to resolve the issue using manuals and technical recipes. If the problem is solved at this stage, the ticket is closed. If not, or if the issue originates from network KPIs, a technical team ticket is created. Problem severity and impact typically determine the support level (L1-L4) required for telecommunication issues. The ticket is routed to the appropriate technical support group-Transmission, RAN, or Core support-based on the issue. These teams use multiple troubleshooting software tools to diagnose the problem. Depending on the findings, the issue is either resolved remotely, often with the help of product-specific documents, or teams are dispatched to the site. If the problem is solved at this stage, the ticket is closed; if not, the case is routed back for further troubleshooting, creating a feedback loop until resolution is achieved.
However, this structured process has notable shortcomings that impact efficiency and customer satisfaction. The use of static thresholds for detecting network degradation means the system often generates excessive tickets in response to normal KPI fluctuations, such as those caused by weather, rather than genuine faults. Additionally, the troubleshooting and optimization software currently in use requires manual data entry, provides only a partial view of the network, and is not open source, limiting flexibility and integration. These factors contribute to suboptimal solutions and maintain expensive, siloed workflows. As a result, customers experience longer wait times for issue resolution, leading to dissatisfaction, while the organization faces an increased number of tickets and a largely reactive approach to problem management, rather than a proactive one
Art of possible solutions
That is where Sionna and Databricks will be helping you to enhance the current process. Sionna is an open source solution that can help you simulate your network and create a digital twin to test network configuration. Databricks helps you unify synthetic Sionna simulations (e.g., mmWave propagation models) with real-world network telemetry (RAN metrics, core logs) in a single platform, ensuring reproducibility.
While Sionna’s differentiable architecture enables gradient-based optimization of entire communication stacks (PHY to MAC layers), Mosaic AI automates hyperparameter tuning for beamforming algorithms, and Ray accelerates simulations across thousands of scenarios (urban vs. rural or failures simulation such as tower tilt due to storms). You can deploy trained models (e.g., base station failure predictors) as inference endpoints, and compare predictions against Sionna’s degradation curves to prioritize maintenance.
Also, as you ingest real-time cell tower telemetry into Delta Lake, you can trigger Databricks Workflows alerts when metrics deviate from Sionna-predicted baselines (e.g., unexpected signal attenuation) and initiate a predictive maintenance process.
In the next series of articles, we will dive into these use cases and show how Databricks helps you use Sionna.
