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Filtering by Aliasing and its application to Reconfigurable Filtering and Compressive Signal Acquisition

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  • PhD Defenses
When Dec 06, 2012
from 01:00 PM to 03:00 PM
Where Maxwell Room 57-124 Engr IV
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The communication systems community has been working towards integrated Software-Defined Radios (SDRs) and Cognitive Radios (CRs) that can reduce cost and enhance connectivity. In light of the technology bottleneck at the analog-to-digital convertor (ADC) and the inapplicability of off-chip filters, the integrated analog front-end is entrusted with the task of sharp, linear, and programmable signal selection required for SDRs and CRs. Traditional analog filtering techniques, however, incur a high penalty in power consumption, area, and linearity to provide the required sharpness and programmability. Similarly, recent efforts that use compressive sensing to acquire wideband spectra have also faced a bottleneck in the complexity of the analog measurement frontend.

Towards enabling SDRs and CRs, this work proposes a new perspective on the design of anti-alias filters that defies the traditional tradeoff between cost, linearity, and programmability. The technique, termed Filtering by Aliasing (FA), anticipates the aliasing operation at the sampler instead of avoiding it. The pre-sampling circuitry is modulated, using the high-speed switching techniques popular in state-of-the-art receivers, to provide significantly enhanced filtering responses at the sampling instances. The work describes how the FA technique, by varying the resistor of a single-pole passive RC filter for example, provides programmable anti-alias filtering comparable to a 7th-order Butterworth filter.

On the compressive sensing front, this work proposes a new approach to the acquisition of sparse spectra using Random Filtering by Aliasing (RFA). RFA takes advantage of noise in realistic spectra to simplify the analog measurement stage, moving most of the complexity to the low-cost, highly reconfigurable digital domain. As a result, RFA achieves significantly better resolution, lower cost, and better programmability than existing schemes.



Mansour Rachid received his B.E. degree in Computer and Communications Engineering from the American University of Beirut (AUB) in 2006 and his M.S. degree in Electrical Engineering from the University of California Los Angeles (UCLA) in 2008. He held internship positions with Silvus Technologies, Inc. in 2008 and with Maxlinear, Inc. in 2011. Mansour has received several academic awards and fellowships. He is currently a PhD candidate at UCLA under the supervision of Prof. Babak Daneshrad. Mansour’s research interests revolve around mixed signal processing techniques.

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