A year ago, NVIDIA open sourced its Aerial wireless software, joining Sionna and a broader suite of open source tools for accelerating next-generation 6G network development. Together, they give researchers a foundation for testing ideas, comparing results and building on each other's work - connecting simulation and AI training with experiments on real over-the-air wireless systems.Today, researchers and developers are using these tools to build 6G-ready networks that do more than just transmit data - receiving signals more effectively, keeping robots and autonomous systems connected when it matters most, and adapting in real time as devices move through the world.
Over the past year, global developer adoption of NVIDIA AI Aerial and NVIDIA Sionna software has grown 10x, while NVIDIA's open source 5G/6G tools have surpassed 1 million downloads. More than 8,000 research and academic institutions worldwide are using these platforms.
The projects emerging today offer a view of how future networks could look. Each addresses a different challenge, but together point toward a broader ambition for building AI-native 6G networks that deliver connectivity, computing and sensing over a converged software-defined platform for billions of people, machines and agents.
Networks That Help Robots React Faster A robot picking up an object needs to understand its surroundings and act before they change. When the AI powering that decision runs on a nearby server or in the cloud, the wireless connection becomes part of the response time.
Researchers are exploring a more efficient approach called goal-oriented semantic communications. Instead of sending full images, robots transmit compact descriptions of objects, their positions and their relationships. Transmitting just as much information as needed allows robots to more quickly respond, especially in dynamic environments like workplaces and warehouses.
At King's College London, researchers connected a camera-equipped robot arm to an NVIDIA Jetson AGX Orin running NVIDIA Sionna, which supports both software-defined 5G network and edge AI processing. In fruit-packing and cube-stacking experiments with deliberately introduced faults, the approach reduced robot task completion time by up to 52.6% and improved task success compared with sending periodic images.
In addition to quicker responses and less processing overhead, the system also proved more resilient - detecting dropped objects, recovering from unsuccessful actions and, in configurations using a digital twin, checking planned movements for potential collisions before execution.
Networks That Learn to Receive Signals Better For a firefighter receiving live drone footage or a rescue team searching for a missing person, timely images can help guide urgent decisions. But interference and changing radio conditions can make wireless signals harder to decode, causing errors and retransmissions that delay information from the field.
AI receivers address this challenge by learning to recover information from incoming signals more effectively. Better reception could help responders maintain a clearer, more current view of a fire scene or search area.
SoftBank demonstrated this by integrating a neural receiver into NVIDIA Aerial CUDA-Accelerated RAN (ACAR) and testing it with a commercial 5G smartphone. In controlled tests with interference, the neural receiver delivered higher uplink throughput than a conventional receiver at all 15 tested settings, with a maximum gain of roughly 60%.
Another way to improve reception is to teach receivers about the environments where they operate. Using NVIDIA Aerial Testbed, ETH Zurich researchers found that tuning neural receivers using local measurements reduced block error rates by more than one-third compared with selected pretrained versions.
Networks That Learn When to Save Power As 6G connects more devices and supports more AI applications, managing energy demand will become increasingly important.
For example, a network may need enough capacity to serve a busy stadium, but demand changes when the crowd leaves. AI can help identify when spare capacity can power down - and when it needs to come back online - so operators can reduce electricity use without dropping connections.
At the University of Leeds, researchers developed a system that predicts network demand 10 minutes ahead and uses those forecasts to guide base station sleep decisions. Before powering down, it checks that neighboring active stations can continue serving affected devices and monitors for changes that require capacity to be restored.
The team tested their approach using a portable, NVIDIA DGX Spark-powered 5G network-in-a-box alongside a digital twin for validating decisions against real wireless equipment. The result: researchers reported a 45.6% reduction in RAN power consumption relative to a full-power baseline while maintaining device connectivity.
Networks That Can Be Tested Faster - Before They're Built Before deploying a network, researchers need to know how it will perform when hundreds of devices compete for connections or radio signals run into interference. Simulating these conditions helps teams evaluate designs before committing to costly equipment and field trials. But realistic simulations can take hours, limiting how many ideas researchers can explore.
The Centre Tecnol gic de Telecomunicacions de Catalunya (CTTC) is accelerating that process by bringing GPU-accelerated radio channel models, running on ACAR, into CTTC's open source 5G-LENA network simulator. These models calculate how wireless signals travel between antennas and devices - a computation that grows increasingly demanding as simulated networks grow.
In an early evaluation, the approach reduced simulation runtime from approximately three hours to 10-20 minutes, depending on
