Electrical & Computer Engineering, Department of

 

First Advisor

Andrew Harms

Date of this Version

7-2018

Document Type

Article

Citation

D. Lai, "Multiuser Coding And Signal Processing In A Low Power Sensor Network," M.S. thesis, University of Nebraska-Lincoln, Lincoln, NE, United States, 2018.

Comments

A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Electrical Engineering, Under the Supervision of Professor Andrew Harms. Lincoln, Nebraska: July, 2018

Copyright (c) 2018 Dongqi Lai

Abstract

Backscatter communication system is a wireless communication system that is used by both academic community and industry circles in recent years. This communication system only requires ultra-low power usage and simple design of the sensors. This project is using backscatter communication system to transmit data with backscatter tags. The method we used is semi-passive backscatter communication. This project focuses on transmitting signals with multiple sensors so there is a problem about distinguishing the signal reflected by different nodes. We modulated the transmitting digital signal with Walsh function to solve the problem of separating the signals between different nodes. By using spread sequences we have interferences between different signals from each node and also from the bouncing and direct path signals. We want to estimate the channel between the sensors to suppress the effect of the interferences. In addition, to make the system more practical with multiple usages and applications, we made the receiver and the illuminator on a moving platform. With this dynamic system it is important to deal with the interference of bouncing signals by analyzing the Doppler shift of received signal. With these approaches the purpose of this project is having the reader of the sensor network to communicate with multiple nodes with backscatter communication. This system can be used on variety of applications such as environmental sensing, signal recording and data communicating with less power usage compared with traditional communication systems.

Advisor: Andrew Harms

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