The future NextG wireless standard must be able to sustain ultra-dense networks with trillions of untrusted devices, many of which will be mobile and require assured high-bandwidth links. This talk explores how deep learning, specifically deep convolutional neural networks (CNNs), will play a critical role to enable secure, high-bandwidth links while minimizing complex, upper layer processing and exhaustive search of the state space. First, we describe how device identification can be performed at the physical layer by learning subtle but discriminative distortions present in the transmitted signal, also called as RF fingerprints. We present accuracy results for largest the radio population (over 10K devices) ever reported in the literature as well as datasets collected from community-scale NSF PAWR platforms. Second, we we show how beam selection for millimeter-wave links in a vehicular scenario can be expedited using out-of-band multi-modal data collected from an actual autonomous vehicle equipped with sensors like LiDAR, camera images, and GPS. We propose individual modality and distributed fusion-based CNN architectures that can execute locally as well as at a mobile edge computing center, with a study of associated tradeoffs. In closing, we provide a glimpse of other systems-centric works that leverage CNNs, such as beamforming with unmanned aerial systems and shaping the wireless environment through reconfigurable intelligent surfaces.
Wireless Networking and Communications Group Seminar
Location: EER 3.646