Multi-Agent Reinforcement Learning Approaches to RF Fingerprint Enhancement

ABSTRACTDeep learning based RF Fingerprinting has shown great promise for IoT device security. This work explores various multi-agent reinforcement learning approaches to enable RF Fingerprint enhancement for an ensemble of transmitters. A RiftNetTM Reconstruction Model (RRM) is used to learn a latent Wi-Fi signal representation and how to reconstruct from that latent representation at the transmitter such that the reconstruction uniquely excites parts of the front-end to enhance the fingerprint. Deep reinforcement learning is then employed to learn the RRM control policy. Details on the design of the control interface, state representation, and rewards structure are presented for four different policy approaches. The resulting computational and security characteristics are discussed.