The Grainger College of Engineering

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Suraj Jog

  • Advisor:
    • Prof. Haitham Hassanieh
  • Departments:
  • Areas of Expertise:
    • Applied AI/ML for Wireless Networks
    • IoT and Low Power WANs
    • RF Sensing
    • Wireless Networking
  • Thesis Title:
    • SCALABLE MILLIMETER WAVE WIRELESS NETWORKS
  • Thesis abstract:
    • The next generation of wireless technologies will provide unprecedented capabilities -- gigabyte communication speeds at ultra-low latencies, hyper-precise localization, and vision-like perception. This will enable a plethora of new applications like wireless virtual and augmented reality, self-driving cars, space communications, precision agriculture, high-performance computing, and more. However, while these performance leaps have been demonstrated in the context of constrained networks with single users and controlled environments, the question of scaling these next-gen wireless technologies to large networks in the wild consisting of multiple heterogeneous nodes remains unsolved. In this talk, I will present three examples of my research that addresses these scalability challenges across different applications, each with unique objectives and constraints. First, I will talk about enabling extreme dense spatial packing of users for untethered wireless streaming in multi-user VR and AR applications, where we can scale the wireless network data rate with the number of clients without suffering interference. Second, I will discuss the challenges of scaling hyper-precise localization enabled by the high bandwidth 5G cellular technologies to ubiquitously deployed low power IoT nodes in the wild. I will show how we can leverage RF-acoustics microsystems to design new kinds of RF filters that can preserve the high localization resolution on narrowband IoT devices that sample 16x below Nyquist. Finally, I will also discuss interdisciplinary research avenues where chip-scale millimeter-wave wireless networks promise to revolutionize new application domains like High-Performance Computing. I will demonstrate how we can leverage deep reinforcement learning and AI tools to learn and generate new networking protocols for the wireless interconnects on multicore processors, which in turn will enable multicore processors to scale to hundreds and thousands of cores. I will conclude the talk with future directions in next-gen cellular and wireless research, both in terms of core methods as well as applications.
  • Downloads:

Contact information:
sjog2@illinois.edu