Physical-Layer Fingerprinting of LoRa devices using Supervised and Zero-Shot Learning

This dataset contains all raw signals (complex float I/Q samples) used in the LoRa fingerprinting experiments of the paper entitled "Physical-Layer Fingerprinting of LoRa devices using Supervised and Zero-Shot Learning". There are 4 databases included: lora1msps, lora2msps, lora5msps, and lora10msps. Each document in the databases is a symbol extracted from a 4-byte random payload LoRa frame, transmitted by a RN2483 radio and received by a USRP B210 sampling at a rate corresponding to the database name. A total of 22 different transmitters were used. For more information, please consult the paper. The document structure is as follows: _id: Unique MongoDB document ID chirp: Base 64 encoded binary float complex I/Q data field: Symbol location inside a LoRa frame tag: Name of the device that sent the frame date: Time and date of reception fn: Frame number rand: Random number for sorting How to import Extract the tar archive. Inside the directory, run the following command to import the lora2msps database: mongorestore --gzip -d lora2msps ./lora2msps This process can be repeated for each dataset. Alternatively, all datasets can be imported automatically by executing: mongorestore --gzip . How to use After the data has been imported, an experiment can be run by simply providing the corresponding config file to tf_train (see https://github.com/rpp0/lora-phy-fingerprinting), e.g.: ./tf_train.py train conf/experiment_lora2msps_mlp.conf