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 state 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 various approaches, debating whether the satellite receiver should autonomously localize interference sources or instead initiate a centralized algorithm for optimizing resource allocation.

Week 4 (6/17 - 6/20)

We explored on concepts related to the interference effect of 5G base station and its UE's (User Equipment) on a Digital Broadcast Satellite Receiver near it. We have researched and formulated ideas to propose a multimodal learning model to analyze the interference and measure the SINR.

We devised our Experiments into 3. Experiment 1 - Satellite Communication, Experiment 2 - 5G Emulation, Experiment 3 - Network Coexistence.

We are in a stage of developing an efficient energy detector spectrum to read the Fast Fourier Transform and Power spectral density which eventually lead to a train a model to predict SINR..

We worked to simulate a Digital Video Broadcasting Satellite Transmitter and Receiver in GNU Radio using SB1(Orbit). We are still working on this as we did not get something that match our expectations.