Nair, Manish233Dang, Shuping234Beach, Mark2024-06-032024-06-032023-07-25Nair, Manish; Dang, Shuping; Beach, Mark (2023). Self Organizing Feature Maps Data Sets. Cranfield Online Research Data (CORD). Dataset. https://doi.org/10.17862/cranfield.rd.23736597https://dspace.lib.cranfield.ac.uk/handle/1826/21780Self Organzing Feature Maps [SOFM] Data Sets for LoRa transmitters, generated by the Batch SOFM Competitive Learning algorihtm. In the algorithm, initially, a Kohonen layer of artificial neurons (of dimensions 10x10) trains upon the data set of raw LoRa I/Qs through randomly initialized set of weights. The original ANN then 'self-organize' or cluster into a batch of six 'offspring' ANNs at every epoch. Except in the 1st epoch, the algorithm trains on the set of offspring ANNs and not the raw I/Qs. By the 200th epoch, the extent of cluster is sufficient to produce distinct SOFM patterns corresponding specifically to a particular LoRa I/Q in the raw I/Qs. The raw LoRa I/Q data, comprising of samples from six sources [5 from LoRa modules and 1 from an ARB], collected from a customized RF penetration test-bed, are also provided.CC BY 4.0Unsupervised Machine Learning''RF Fingerprinting'Dataset10.17862/cranfield.rd.23736597