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ECE PhD Proposal Review: Sara Garcia Sanchez
November 30, 2021 @ 12:00 pm - 1:00 pm
PhD Proposal Review: Learning and Shaping the Wireless Environment: An Integrated View of Sensing, Computing and Communication
Sara Garcia Sanchez
Abstract: The explosive growth in Internet of Things (IoT) deployments and anticipated data volumes that will be generated within future autonomous vehicles require collecting and processing large amounts of data, generally transmitted over the wireless channel. In this context, conventional permanent deployments limited to leverage the existing wireless environment, often fall short of meeting the required capacity demand. To address this limitation, this dissertation takes a hands-on approach to equip communication systems with technology to perceive and collaborate with and within the environment. Specifically, we build (i) accurate physics-oriented predictive models and multimode sensing techniques to gain awareness of the existing channel, as well as (ii) novel multidisciplinary approaches to intelligently modify the wireless channel towards the communication link benefit.
In this dissertation, we first prove that combining wireless channel modelling, multimode sensing and robotics provides significant link performance gains. To this extent, we adopt a systems approach to study how millimeter wave (mmWave) radio transmitters on Unmanned Aerial Vehicles (UAVs) provide high throughput links under typical hovering conditions. Based on sensing and modelling efforts, we propose techniques to exploit the information contained in the spatial and angular domains of empirically collected data from GPS, cameras and RF signals. We demonstrate hovering impact mitigation by (i) selecting near-to-optimum transmission parameters as compared to the mmWave standard IEEE 802.11ad and (ii) proposing corrective coordinated actions at the UAVs from the robotic controls. These methods achieve mmWave beam-tracking and robust link deployment under event(s) impacting link performance, such as hovering or blockage in the light of sight between transmitter and receiver.
Then, this dissertation experimentally demonstrates how the wireless environment can be interactively programmed through the use of Reconfigurable Intelligent Surfaces (RIS) to partially offload computation into the wireless domain. In particular, we propose AirNN, a system capable of realizing analog over-the-air convolutions, accurately enough to substitute their digital equivalent in a Convolutional Neural Network (CNN).
As proposed future work, this dissertation will explore innovative uses of the RIS technology in Multiple Input Multiple Output (MIMO) systems for 6G and beyond. Specifically, we will investigate (i) how the use of RIS helps overcome environmental limitations of a highly spatially correlated MIMO channels, and (ii) whether the use of RIS can enable the use of MIMO techniques with a single antenna at the receiver.