The 4-step AI workflow designed to solve complex parameter challenges in modern networks like 5G and Wi-Fi, bypassing the need for manual algorithms.
Data Preparation & Synthesis
AI models require massive amounts of data. To overcome real-world data shortages, engineers synthesize millions of compliant waveforms using MATLAB apps, or capture live over-the-air signals by connecting directly to Software-Defined Radios (SDRs).
AI Modeling: ML vs. DL
Instead of traditional Machine Learning (which requires humans to manually write code to extract features from signals), Deep Learning feeds raw I/Q signal data directly into multi-layered neural networks to handle feature extraction and classification simultaneously.
Simulation & System Testing
Once trained, the AI model cannot exist in a vacuum. It is integrated back into complex, system-level simulations to verify it works properly for critical tasks like identifying modulations, RF Fingerprinting (device identification), and predicting 5G channels.
Deployment & Acceleration
The finalized algorithms are pushed to physical edge devices or enterprise clouds. To handle the massive computational load, code generators automatically convert the MATLAB models into CUDA-enabled code to run efficiently on GPUs.