AI for Engineers — ORAN Infra Inc.

Phase 1 transitions from using generic AI tools to deploying bespoke AI applications designed specifically for the rigors of telecommunications engineering. By 2027, we aim to provide engineers with predictive capabilities that extend beyond traditional simulation.

Neural Signal Processing

Optimization Goal

Beamforming Matrix Prediction

Utilizing Recurrent Neural Networks (RNNs) to predict channel state information (CSI) and optimize beamforming matrices for Massive MIMO systems in real-time.

Cost Function

$$J(\theta) = \sum_{t=1}^{T} ||\mathbf{H}_t \mathbf{W}_t - \mathbf{G}_t||^2 + \lambda ||\theta||^2$$

Preventive Logic

Thermal Drift Compensation

AI-driven compensation for thermal expansion in antenna radiators, ensuring gain stability across extreme temperature gradients.

Expansion Correction

$$\Delta L = L_0 \alpha \Delta T + \epsilon_{AI}(\mathbf{X}, \mathbf{T})$$

Collaboration

University Research

Wave Propagation in Urban Canyons

Partnership with University laboratories to train AI models on ray-tracing data collected from high-density urban environments in Ontario.

LFP Electrolyte Aging Predictions

Deep learning models trained on millions of battery cycle data points to predict remaining useful life (RUL) with >98% accuracy.