Channel Prediction and Transmitter Authentication with Adversarially-Trained Recurrent Neural Networks

Abstract

As wireless communications and interconnected networks become ubiquitous and relied upon, they must also remain secure. Advanced communication systems that use techniques to improve data throughput and minimize latency lend themselves to physical-layer authentication. The stochastic and dynamic nature of the wireless mobile channel provides features that can be extracted through deep learning. We propose a novel method to authenticate transmitters at the physical layer by leveraging channel state information to predict future channel impulse responses. Specifically, we compare the use of recurrent neural networks (RNNs) using long-short term memory (LSTM) and gated recurrent unit (GRU) cells with variations of a conditional generative adversarial network (CGAN) to authenticate transmitters in a mobile environment. Our evaluation shows that standalone RNNs using LSTM and GRU cells are adept at predicting future channel responses, however a CGAN-trained discriminator using GRU cells is able to match the authentication accuracy of a standalone network without using a predefined channel prediction error threshold. Using a discriminator trained by a CGAN with binary cross entropy loss in the discriminator and mean squared error loss in the generator, the neural network was able to authenticate at a 98.5% rate.