Demonstrating Deep Learning Based Communications Systems Over the Air In Practice
Timothy J. O’Shea, Tamoghna Roy, Nathan West, Benjamin C. Hilburn · 2018
Several novel approaches to wireless communications system design have recently been introduced which use deep learning to synthesize and adapt a new class of signal processing systems to the actual data and effects present in the radio environment. Both the autoencoder-based communications system and the feature learning-based radio signal sensor represent significant progress in the ability of radio systems to optimize directly on real-world data samples and distributions. As part of this demonstration, we will show two real-world systems operating over-the-air using these approaches, which we are rapidly maturing at DeepSig to bring theory into practice.