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Self Organizing Feature Maps Data Sets

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posted on 2023-07-25, 07:39 authored by Manish NairManish Nair, Shuping Dang, Mark Beach

Self 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.  


Secure Wireless Agile Networks (SWAN)

Engineering and Physical Sciences Research Council

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