| 9 | | - write about what is RFI (specific to our 5g and satellite problem) |
| 10 | | - goal is to design a ML pipeline that could: |
| 11 | | 1. detect RFI from transmitting signals |
| 12 | | - to do so, we developed algorithms to generate graphical data to train RFI detecting CNNs from existing dataset and also used 5G toolbox to get our own signal data |
| 13 | | 2. then mitigate the interferences by optimizing beam forming |
| 14 | | |
| 15 | | Radio Frequency Interference occurs when overlapping signals, such as those from satellite systems and emerging 5G technologies in the FR3 (7–24 GHz) band, disrupt each other’s communication quality. We aim to develop a machine learning pipeline that can detect and mitigate such interference. To detect RFI, we utilize convolutional neural networks using both graphical data generated from existing datasets and custom 5G signal data produced through MATLAB’s 5G Toolbox. To mitigate interference, we will use beamforming optimization to adaptively minimize RFI impact, enabling reliable coexistence. |
| | 9 | Radio Frequency Interference (RFI) occurs when overlapping signals, such as those from satellite systems and emerging 5G technologies in the FR3 (7–24 GHz) band, disrupt each other’s communication quality. We aim to develop a machine learning pipeline that can detect and mitigate such interference. To detect RFI, we utilize convolutional neural networks that leverage both graphical data generated from existing datasets and custom 5G signal data produced using MATLAB’s 5G Toolbox. For the mitigation aspect, we will apply ML algorithms to optimize beam-forming with real-time data. |
