01
UE swarm
emulation
Execute independent, standards-aware UEs as coordinated GPU workloads while preserving timing, mobility, traffic, channel, and protocol state.
Explore NextUE ↗ AI-native wireless systems
Program the
wireless future.
UniPhy Technologies builds GPU-native instrumentation for AI-RAN and 6G: realistic, observable, multi-device experimentation from waveform to workload.
01 — The UniPhy perspective
Make every
wireless experiment count.
AI-native networks need more than offline simulations. They need a programmable population of realistic devices, a live network, and synchronized ground truth that makes every result explainable and repeatable.
See the platform ↗02 — The NextUE platform
One runtime.
Every device state.
NextUE connects standards-aware virtual UEs, GPU-native PHY processing, programmable channel conditions, and synchronized observability into one scientific instrument.
UE swarm
02
AI-RAN
experimentation
Insert neural wireless functions into a live 5G stack and compare learned and conventional processing under realistic multi-device conditions.
Explore innovation testbeds ↗03
Digital-twin
instrumentation
Capture waveform, channel, protocol, workload, and execution traces for training, evaluation, deterministic replay, and calibration.
See the workflow ↗03 — Flagship DGX testbed
Move AI inference
closer to the device.
UniPhy’s DGX-based MEC testbed sends an image task through an OpenAirInterface 5G path to GPU-accelerated inference at the edge, then returns compact detection results with timing metadata.
Discuss the testbed ↗What we are building and measuring
A real wireless-to-GPU execution loop.
We are connecting an application at the user equipment to an OpenAirInterface 5G network, carrying the task through the user plane, and executing AI inference on an NVIDIA DGX Spark edge node.
Evidence for adaptive offloading.
For every task, we record network, application, and GPU timing so we can quantify when work should run locally, at the edge, or across both.
From one task to many devices.
The initial image-inference workflow is the measured foundation for industrial inspection, multi-device workloads, and closed-loop AI-RAN decisions.
OAI / NVIDIA EDGE TESTBEDFrom wireless packet to edge intelligence.
The current testbed connects a UE application, OAI radio and 5G core components, a local N6 network, and a GPU-enabled MEC service on an NVIDIA DGX Spark edge node.
UE task→OAI 5G path→MEC inference→Result
01
Real 5G user-plane path
Measure an application task across the wireless network rather than simulating the connection offline.
02
GPU inference at the edge
Run a larger AI model close to the device and return lightweight detection metadata.
03
Built for adaptation
Use latency, bandwidth, queue delay, and GPU load to guide future offloading decisions.
Three-stage testbed roadmap
Each stage reuses the verified network and application path from the previous stage.
One image. One measured result.
Send a JPEG task through the OAI PDU session, run YOLO inference on the edge GPU, and return labels, confidence, boxes, and timing.
Offload only what needs deeper analysis.
A lightweight local anomaly model screens samples first, while suspicious images are sent to the MEC service for detailed classification or segmentation.
Let conditions shape the workload.
Adjust local versus edge processing, frame rate, resolution, model, threshold, and in-flight tasks as network and compute conditions change.
05 — The experiment loop
Observe.
Understand. Repeat.
01
Configure
Define UE populations, mobility, channels, traffic, and AI workloads.
02
Execute
Run coordinated GPU workloads against real gNB and SDR infrastructure.
03
Replay
Use synchronized traces to explain outcomes and validate the next idea.
04 — Where we make a difference
Built for
the intelligent edge.
UniPhy helps teams move from algorithm development to evidence-backed deployment in the environments where wireless reliability matters most.
01
AI-RAN & network demonstrations
↗02
Autonomous systems
↗03
Private & industrial networks
↗04
Public safety & defense
↗05 — The people behind UniPhy
Deep expertise.
Shared ambition.
UniPhy Technologies brings together technical leadership and systems engineering to turn next-generation wireless ideas into working instruments.
06 — Start a conversation
Ready to test
what’s next?
Tell us about the wireless system, AI-RAN experiment, or edge application you’re building.