|   | 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. |