Week 1 (5/28 - 5/30)

We installed and familiarized ourselves with GNU Radio.

We also explored the architecture of the Orbit test bed.

Reviewed several papers to gain insights into the current scenario of 5G networks and the interference mitigation techniques used across different frequency ranges.

Week 2 (6/03 - 6/06)

We delved into the context of HyPhyLearn and conducted an in-depth exploration of Domain Adversarial Neural Networks (DANN).

Ran reference code for DANN and examined the source, target, and domain accuracies.

Analyzed graphs that demonstrated the model's ability to learn from domain-invariant features.

Week 3 (6/03 - 6/06)

We explored the TensorFlow implementation of the HyPhyLearn model, which classifies 2D Gaussian datasets.

Augmented the code from TensorFlow 1.0 to PyTorch and validated the experimentation.

Additionally, we explored how Dynamic Exclusion Zones apply to our mitigation objective. We weighed in different ideas regarding whether the satellite receiver will perform localization of interference sources or rather trigger a centralized algorithm that will optimize resource allocation.