Sara Garcia Sanchez’s PhD Dissertation Defense

“Learning and Shaping the Wireless Environment: An Integrated View of Sensing, Computing and Communication”

Abstract:

The explosive growth in Internet of Things (IoT) deployments and anticipated data volumes that will be generated within future autonomous devices require collecting and processing large amounts of data, generally transmitted over the wireless channel. Rigid infrastructure deployment that does not adapt to the changing wireless environment is not well suited to handle these new demands. To address this limitation, this dissertation takes a hands-on approach to equip communication systems with technology to learn from, interact with and actuate within the environment. Specifically, we build (i) accurate physics-based predictive models and multimodal sensing techniques to gain awareness of the existing channel, as well as (ii) novel multidisciplinary approaches to intelligently shape the wireless channel towards enhancing the communication link.

We first prove that combining wireless channel modeling, multimodal 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 modeling 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 how to mitigate the impact of hovering 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.
Finally, we experimentally demonstrate how the wireless environment can be interactively shaped through the use of Reconfigurable Intelligent Surfaces (RIS). First, we propose AirNN, a system capable of partially offloading computation into the wireless domain by realizing analog convolutions with over-the-air computation. We demonstrate that such computation is accurate enough to substitute its digital equivalent in a Convolutional Neural Network (CNN). Second, we propose a RIS-based spatio-temporal signal modification approach for channel hardening (i.e., ensure low power fluctuations in the received signal) in a Single-Input Single-Output link and under rich multipath, which is common for IoT 5G+ deployments. We prove that our approach achieves channel hardening similar to a classical Single-Input Multiple-Output (SIMO) system while only using a single antenna element at the receiver end.

All the above theoretical advances are validated with rigorous analysis and experimentation.

Committee:

Prof. Kaushik Chowdhury (Advisor)

Prof. Stefano Basagni

Prof. Josep Jornet