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 code of the HyPhyLearn model, which classifies 2D Gaussian datasets. Augmented the code from TensorFlow 1.0 to PyTorch and validated the experimentation. Additionally, we conducted research on works with similar use cases and reviewed research papers to gain proper knowledge on setting up a physical model for generating synthetic data.