Members: Atharv ThakurUG, Anant CharyUG
Faculty advisors: Predrag Spasojevic
Designing unsupervised learning techniques for the purposes of radio source clustering and packet edge detection.
Week 1 (05/26-05/28)
Read textbooks on digital communications and spectral analysis, as well as papers and theses on the topic that used either EM algorithms with tensor decompositions or the mean shift algorithm. Also read the GNU radio user manual and implemented and tested automated data transfer to lab equipment for future experimentation.
Week 2 (06/01-06/04)
Finished reading material on spectral analysis and digital communications and continued reading various papers on shape optimization and edge detection methodologies for detecting rectangles. Completed design of the hardware spectrum analyzer in SystemVerilog and began writing a testbench that will allow data streaming from files. Also, created GNURadio simulation that generates a spectrogram of interfering WiFi, Bluetooth, and 5G Cellular packets to use as a visualization tool and run image processing algorithms on.
Week 3 (06/08-06/11)
We completed mathematical formulation of our two stage algorithm and developed the iterative steps that it will take to identify the rectangles. We developed the iterative steps that the algorithm will take to identify the rectangles and decided on criteria for splitting and moving to the next stage. We also begun the software model that we will use to verify the data using spectrogram images generated through GNURadio.
Week 4 (06/15-06/18)
We completed the software model (pre-splitting) in MATLAB first stage of the algorithm and tested it on several spectrogram images. We also created a more accurate simulation of WiFi, Cellular, and Bluetooth packets in GNURadio using the 802.11be WiFi 7, Bluetooth BLE, and 5G NR n78 bands specifically.
Week 5 (06/22-06/25)
We completed the software model and tested it on the 5 GHz WiFi band. The splitting algorithm, culling procedure, and packet removal were all developed and tested. We found that the algorithm takes a few iterations to finish detecting all packets in the frequency band.
Week 6 (06/29-07/02)
We added horizontal splitting to the algorithm and refined the algorithm procedures. We thoroughly documented the algorithm in a slideshow and made a flowchart that explains the process of the algorithm to present to the ARL (Army Research Lab). We also began work on a machine learning model that will use a labeled dataset to predict the coefficient parameters for the algorithm's objective function.