Cobra Alemdar PhD Dissertation Defense

Name:
Kubra Alemdar

Title:
Overcoming and Engineering Wireless Signals for Communication and  Computation

Date:
8/7/2024

Time:
11:00:00 AM

Committee Members:
Prof. Kaushik Chowdhury (Advisor)
Prof. Josep Jornet
Prof. Marvin Onabajo

Abstract:
The phenomenal growth of connected devices, especially rapid expansion of IoT networks and the increasing demand for wireless services are the main driving forces for the evolution of wireless technologies. However, the realization of such technologies requires a radical transformation of existing infrastructures to satisfy the needs of changing wireless environments. The main limitation in delivering these systems stems from a vast diversity in their demands and constraints. To address this limitation, this dissertation shows how wireless signals and their interaction with and within the wireless propagation domain can be used as communication or computational tools that enable us to achieve certain novel tasks. Specifically, we build i) cross-functionality architectures to engineer the wireless channel to a) enable the operation of emerging technologies, and b) demonstrate a new paradigm for computing with wireless signals, and ii) intelligently shape the wireless channel to create reliable communication links. This dissertation presents an experimentally validated software-hardware systems with thorough analysis, delivering the following key advancements with distinct contributions:

First, We present an innovative physical layer solution for distributed networks that provides over-the-air (OTA) clock synchronization, known as RFCLOCK, to overcome the hurdle of implementing fine-grained synchronization for emerging technologies. We first develop the theory for such precision synchronization, and second implement it in a custom-design, compatible with commercial-off-the-shelf (COTS) software-defined radios (SDRs). We compare the performance of RFClock with popular wired and GPS-based hardware solutions, both in terms of clock performance as well as impact on distributed beamforming.

Next, we propose two novel approaches, utilizing reconfigurable intelligent surfaces (RISs) to ensure reliable connectivity in wireless networks by controlling the propagation environment: i) we present RIS-based spatio-temporal approach to enhance the link reliability for IoTs where sensors are small-factor designs with single-antenna in a rich multipath environment. We demonstrate the design of RIS and how it can effectively perturb the environment, generating multiple wireless propagation channels and achieving the performance of a multi-antenna receiver in a Single-Input Single-Output (SISO) link. We compare the performance of the system with a multi-antenna receiver in terms of channel hardening and outage probability. ii) We introduce REMARKABLE, an online learning based adaptive beam selection strategy for robot connectivity that trains kernelized multi-armed bandit (MAB) model directly in real-world settings of a factory floor. We show how RISs with passive reflective elements can create beamforming towards target robots, and provide a solution to the problem of adaptive beam selection in dynamic channel conditions. We experimentally demonstrate that REMARKABLE can achieve a significant reduction in beam selection time compared to classical approaches and adaptive beam selection in mobility settings.

Finally, we introduce AirFC, a system harnessing the capability of OTA computation to run inference on a neural network (NN) consisting of a set of fully connected layers (FC) by leveraging multi-antenna systems. We experimentally demonstrate and validate that such computation is accurate enough when compared to its digital counterpart.