Deepmod: An Over-the-Air Trainable Machine Modem for Resilient PHY Layer Communications

Abstract

Traditional physical layer protocols (e.g. WiFi, WiMax, etc.) are well established and are often optimal in a wide variety of channel conditions including heterogenous links and in tactical communications. Unfortunately, this same optimality encourages ubiquity in wireless communication technology and enhances the potential for catastrophic cyber or physical attacks due to prolific knowledge of underlying physical layers. Any truly resilient communications protocol must be capable of immediate redeployment to meet quality of service (QoS) demands in a wide variety of possible channel media. This work proposes an approach to communications that is contrary to much traditional approaches in that processing blocks are generated real-time and only relevant to the particular channel medium being used. Rather than creating man-made ubiquitous blocks of signal processing, we examine using processing that is immediately expendable once it has been used. This is achieved through software-defined radios, and deep modulation, where system blocks are replaced with machine learning graphs that can be trained, used, and then discarded as needed. Simulation and experimental hardware show how deep modulation can converge to viable communications links, using the same machine intelligence, in vastly different channels.