| | 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. |