This LTE_RFFI project sets up an LTE device radio frequency fingerprint identification system using deep learning techniques. The LTE uplink signals are collected from ten different LTE devices using a USRP N210 in different locations. The sampling rate of the USRP is 25 MHz. The received signal is resampled to 30.72 MHz in Matlab and is saved in the MAT file form. The corresponding processed signals are included in the dataset. More details about the datasets can be found in the README document.

This dataset includes real-world time-series statistics from network traffic on real commercial LTE networks in Greece. The purpose of this dataset is to capture the QoS/QoE of three COTS UEs interacting with three edge applications. Specifically, the following features are included:  Throughput and Jitter for each UE-Application and Channel Quality Indicator (CQI) for each UE. The interactions were generated from a realistic network behavior in an office by developing multiple network traffic scenarios.

Opening up of the CBRS band for the secondary users' transmissions poses challenges in the protection of incumbent radar users from co-channel interference. The use of Machine Learning algorithms for addressing these challenges requires representative real-world datasets.This dataset contains overlapping radar and LTE signals captures over-the-air in the shared CBRS band using an experimental testbed composed of software defined radios in RF anechoic chamber.

Image : Image was made by me for other International Contest (held by some Medical Institute,USA in the year 2021), 'An intuitive of electromagnetic radiation flowing over epithelial tissue'.

This dataset is in support of my Research, Discovery and Claim. 

I have claimed 2-3 dozen novel claims (some of which are already on IEEE PrePrint Server)