While wireless communications technology has advanced considerably since its invention in the 1890s, the fundamental design methodology has remained unchanged throughout its history – expert engineers hand-designing radio systems for specific applications. Deep learning enables a new, radically different approach, where systems are learned from wireless channel data. This talk will provide a high-level overview of deep learning applied to wireless communications, discuss the current state of the technology and research, and present a vision for the future of wireless engineering using a data-centric approach.
Nathan West is the Principal Engineer at DeepSig Inc., which is commercializing the fundamental research behind deep learning applied to wireless communications and signal processing. He also contributes to GNU Radio, maintaining a component called VOLK which provides highly optimized signal processing routines. He is currently pursuing a Ph.D. in Electrical Engineering at Oklahoma State University focused on machine learning sensing systems for RF signals and lives in Washington, D.C.