RFix - Terrain-Aware RF Signal Simulation Platform

Generate labeled IQ data from terrain-aware RF scenarios with analysis tools and APIs to mass-produce datasets for testing and ML training.

Terrain-Aware RF Simulation

RFix enables engineers and researchers to create complex wireless test scenarios through an intuitive visual interface and API-driven workflows. Build node-based transmission chains, place emitters and receivers in terrain-aware environments, apply propagation behavior, and mass-produce labeled IQ datasets for receiver testing, algorithm validation, and machine learning training.

Product Capabilities

  • Visual Node Library: Chain waveform sources, protocol emitters, channel models, analysis tools, and export blocks in one graph
  • Terrain Infrastructure: Use ITU-R propagation models and Sionna terrain workflows to evaluate wireless scenarios earlier
  • Map-Based Placement: Review emitters, receivers, sensors, and terrain overlays in the spatial context of the operating area
  • RF Recording Analysis: Inspect generated or captured IQ data with spectrogram and time-domain analysis views
  • Export Workflows: Export raw IQ recordings, SigMF pairs, and portable RFix project files for downstream tools
  • API Dataset Generation: Reuse scenarios in repeatable configs and batch-oriented API workflows to mass-produce data for testing and ML training

Why Choose RFix for RF Testing?

Save Time: Compare signal chains, terrain assumptions, emitter placements, and receiver locations before field deployment.

Reduce Risk: Validate RF systems with realistic scenarios that include propagation effects, interference, fading, and environmental constraints.

Inspect Results: Use built-in spectrogram and time-domain views to verify generated recordings before export.

Integrate Downstream: Feed generated recordings and metadata into SDR, DSP, VSG, RFML, and external validation toolchains.

Use Cases

RFix is used by RF engineers, researchers, and developers for receiver testing, algorithm validation, labeled dataset generation, ML training data production, spectrum monitoring simulation, RF recording analysis, and terrain-aware wireless design. The platform supports applications from simple waveform generation to complex multi-signal scenarios with map-based placement, channel behavior, and API-scale dataset production.