Source code

RF Fingerprinting

With the widespread adoption of the Internet of Things (IoT), the number of wirelessly connected devices will continue to proliferate over the next few years. Along with this increase, device authentication  will become more challenging than ever specially for devices under stringent computation and power budgets.  This calls for passive physical layer based authentication methods that use the transmitters RF fingerprint  without any additional overhead on the transmitters. RF fingerprints arise from the transmitters’ hardware variability and the wireless channel and hence are unique to each device. Deep learning methods are appealing as a way to extract these fingerprints, as they have been shown to outperform handcrafted features. Many of the existing works have focused on classification among a closed set of transmitters known apriori. However, an intruder can be any device outside of this set. In this project our objective are as follows:


Modulation classification

With the widespread adoption of the Internet of Things (IoT), the number of wirelessly connected devices will continue to proliferate over the next few years. Along with this increase, device authentication  will become more challenging than ever specially for devices under stringent computation and power budgets.  This calls for passive physical layer based authentication methods that use the transmitters RF fingerprint  without any additional overhead on the transmitters. RF fingerprints arise from the transmitters’ hardware variability and the wireless channel and hence are unique to each device. Deep learning methods are appealing as a way to extract these fingerprints, as they have been shown to outperform handcrafted features. Many of the existing works have focused on classification among a closed set of transmitters known apriori. However, an intruder can be any device outside of this set. In this project our objective are as follows: