| 1 | |
| 2 | == **Week 1 (5/28 - 5/30)** |
| 3 | |
| 4 | We installed and familiarized ourselves with GNU Radio. |
| 5 | We also explored the architecture of the Orbit test bed. |
| 6 | Reviewed several papers to gain insights into the current scenario of 5G networks and the interference mitigation techniques used across different frequency ranges. |
| 7 | |
| 8 | |
| 9 | == **Week 2 (6/03 - 6/06)** |
| 10 | |
| 11 | We delved into the context of HyPhyLearn and conducted an in-depth exploration of Domain Adversarial Neural Networks (DANN). |
| 12 | Ran reference code for DANN and examined the source, target, and domain accuracies. |
| 13 | Analyzed graphs that demonstrated the model's ability to learn from domain-invariant features. |
| 14 | |
| 15 | |
| 16 | == **Week 3 (6/03 - 6/06)** |
| 17 | |
| 18 | We explored the TensorFlow code of the HyPhyLearn model, which classifies 2D Gaussian datasets. |
| 19 | Augmented the code from TensorFlow 1.0 to PyTorch and validated the experimentation. |
| 20 | 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. |