Deep Learning for Real Time Antenna Array Failure Recovery

We present an exceptional AI-Assisted framework for fast and robust recovery of phase failures in planar antenna arrays. Using a U-Net-based neural architecture trained to replicate the results of gradient descent optimization we effectively remove the reliance on time-consuming optimization methods for in situ recovery. Our approach is able to be adapted to any array geometry and provides failure recovery for up to 50% failure rate. Our method recovers on average over 90% of the performance achieved by an equivalent optimization-often matching or exceeding key directivity metrics. The model's adaptability allows the user to dictate in the training process what array characteristics need recovered, such as null placement and sidelobe suppression, making it highly effective even in high-failure scenarios. Leveraging GPU-Accelerated neural network predictions, we reduce computation times dramatically, achieving a remarkable 67x in situ speedup over conventional optimization methods.