Series in Microelectronics
edited by Wolfgang
Fichtner
Qiuting Huang
Heinz Jäckel
Gerhard Tröster
Bernd Witzigmann
Patrick Mächler,
VLSI Architectures for
Compressive Sensing and
Sparse Signal
Recovery.
2013. XII, 160 pages. € 64.00.
ISBN 978-3-86628-446-3
Abstract:
The introduction of compressive sensing (CS) led to a new paradigm in
signal processing. Traditionally, signals are sampled above their Nyquist rate. Using CS, the same information is acquired
with much fewer measurements, provided a sparse representation of the signal
exists. This makes CS a very promising technology with a large number of
potential applications.
While the acquisition of measurements is simplified, the reconstruction
of the original signal becomes more involved. Sparse signal recovery algorithms
solve the corresponding systems of under-determined linear equations and have
proven very efficient for various applications. Examples include de-noising,
the restoration of corrupted signals, signal separation, super-resolution, and
in-painting. All applications are based on the observation that many natural
and man-made signals have sparse representations in some suitable bases.
In the last few years, impressive progress has been made in the
development and characterization of fast recovery algorithms. However, the
computational effort for successful signal recovery remains high, even for
problems of moderate size. Reconstruction becomes especially challenging for
real-time applications with stringent power constraints, e.g., on mobile
devices. Such applications require efficient hardware implementations of sparse
signal recovery algorithms, which we develop in this thesis. We present
different architectures of greedy algorithms for a number of selected
applications.
The first example is the estimation of sparse channels in broadband
wireless communication. The use of sparse recovery algorithms efficiently
reduces noise and, thus, increases estimation quality. Architectures for three
algorithms are developed and their realizations in ASICs are compared. We show
that approximative algorithms deliver good results at
low hardware complexity.
The second application is the recovery of signals corrupted by structured
noise. Using the example of audio restoration from corruptions by clicks and
pops, fast realizations of the approximate message passing algorithm are
designed. Two fundamentally different architectures -one relying on fast
transforms, the other relying on parallel processing power- are developed and
compared. Large gains in terms of throughput and circuit complexity are
realized by applying fast transforms in the context of CS recovery. The choice
of the most attractive algorithms and architectures depends on the sparsity, the number of measurements, and the basis in
which the samples are taken. In general, approximate message passing is found
to be very well suited for hardware recovery of moderately sparse signals while
serial greedy pursuits are better suited for very sparse signals.
Further, a new application of CS in localization is explored. We show how
sparse recovery increases the detection accuracy in passive radar systems based
on WiFi signals.
Finally, also a new sensing device, acquiring measurements with very low
hardware complexity, is introduced. This modified analog-to-digital converter
samples at non-uniformly distributed points, which allows the reconstruction of
Fourier-sparse signals from very few measurements. All the presented examples
and hardware implementations bring CS one step closer to practical
applications.
Patrick Mächler was born in Zurich, Switzerland, in 1984. He received his MSc degree in information technology and electrical engineering
from ETH Zurich, Switzerland, in 2008. In 2008, he was a visiting researcher at
Berkeley Wireless Research Center (BWRC), UC Berkeley, CA, USA. In the same
year, he joined the Integrated Systems Laboratory of ETH Zurich as a research
assistant. His research interests include digital signal processing, VLSI
architectures, compressive sensing, and wireless communication.
Keywords: Application specific
integrated circuit (ASIC), channel estimation, compressive sensing,
localization, signal restoration, sparse signal recovery
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