Publication Date
2015
Document Type
Thesis
Committee Members
Henry Chen (Committee Chair), Marian Kazimierczuk (Committee Member), Jiafeng Xie (Committee Member)
Degree Name
Master of Science in Engineering (MSEgr)
Abstract
In order to accurately reconstruct signal waveform a signal must be sampled at least twice as fast as the bandwidth of the signal. Ultra Wideband (UWB) signals have extraordinary potential for high information transmission while a central focus of wireless has been the mobile communication. It is an emerging area that involves development of RF sensing and spectral applications over multiple GHz bandwidths. Even though our technology is improving, it is very challenging to build ADC's that are compatible and keep up with the growth of ultra-wideband range. Compressive sensing does "sampling" and "compressing" at the same time and exploits the sparsity for commensurate power saving by enabling sub-Nyquist under-sampling acquisition. The main idea behind compressive sensing is to recover specific signals from very few samples as compared to conventional Nyquist samples. In this thesis, a compressive sensing front-end (CSFE) is designed and analyzed to mitigate sampling approach limitations of the architecture in a CMOS process. CSFE has four main components: pseudo random sequence generator (PBRS), multiplier, integrator, and ADC. The PBRS (implemented by a Gold code generator) and the multiplier are designed in Cadence Spectre using TSMC 180nm technology. The integrator and the 10-bit ADC are designed and verified using both Verilog-A and Matlab. Using 4 GHz PBRS and 800 MHz under sampling ADC, the CSFE design can detect signal frequency up to 2 GHz after applying the Orthogonal Matching Pursuit algorithm to reconstruct the under sampling ADC data.
Page Count
66
Department or Program
Department of Electrical Engineering
Year Degree Awarded
2015
Copyright
Copyright 2015, some rights reserved. My ETD may be copied and distributed only for non-commercial purposes and may be modified only if the modified version is distributed with these same permissions. All use must give me credit as the original author.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.
