Theses Doctoral

Signal Processing for Wireless Power and Information Transfer

Zhong, Shan

The rapid development of the Internet of Things (IoT) and wireless sensor network (WSN) technologies enable easy access and control of a variety forms of information and data from numerous number of smart devices, and give rise to many novel applications and research areas such as smart home, machine type communications, etc. However due to the small sizes, sophisticated environment, and large number of devices in network, it is hard to directly power the devices from grid. Hence the power connectivity remains one of the major issues that needs to be addressed for related IoT applications. Wireless power transfer (WPT) and backscatter communications are provisioned to be prominent solutions to overcome the power connectivity challenge, but they suer strong efficiency limitation which becomes the barrier to universally popularize such technologies. On the other hand, network optimization is also a research focus of such applications which significantly affects the performance of the system due to the high volume of connected devices and different features. In this thesis we propose advanced techniques to overcome the challenges on the low efficiency and network design of the wireless information and power transfer systems. The thesis consists of two parts. In the first part we focus on the power transmitter design which addresses the low efficiency issue associated with backscatter communication and WPT. In Chapter 2, we consider a backscatter RFID system with the multi-antenna reader and propose a blind transmit and receive adaptive beamforming algorithm. The interrogation range and data transmission performance are both investigated under such configuration. In Chapter 3 we study wireless power transfer by the beamspace large-scale MIMO system with lens antenna arrays. We first present the WPT model for the beamspace MIMO which is derived from the spatial MIMO model. By constraining on the number of RF chains in the transmitter, we formulate two WPT optimization problems: the sum power transfer problem and the max-min power transfer problem. For both problems we consider two different transmission schemes, the multi-stream and uni-stream transmissions, and we propose different algorithms to solve both problems in both schemes respectively. In the second part we study the network optimization problems in the WPT and backscatter systems. In Chapter 4, we study the resource allocation problem for a RF-powered network, where the objective is to maximize the total data throughput of all sensors. We break the problem into two subproblems: the sensor battery energy utilization problem and the charging power allocation problem of the central node, which is an RF power transmitter that transmits RF power to the sensors. We analyze and show several key properties of both problems, and then propose computationally efficient algorithms to solve both problems optimally. In Chapter 5, we study the time scheduling problem in RF-powered backscatter communication networks, where all transmitters can operates in either backscattering mode or harvest-then-transmit (HTT) mode. The objective is to decide the operating mode of each transmitter and minimize the total transmission time of the network. We also consider both ideal and realistic transmitters based on different internal power consumption models for HTT transmitters. Under both transmitter models we show several key properties, and propose bisection based algorithms which has low computational complexity that solves the problem optimally. The results are then extended to the massive MIMO regime.


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More About This Work

Academic Units
Electrical Engineering
Thesis Advisors
Wang, Xiaodong
Ph.D., Columbia University
Published Here
October 16, 2019