37 research outputs found
Demodulating Subsampled Direct Sequence Spread Spectrum Signals using Compressive Signal Processing
We show that to lower the sampling rate in a spread spectrum communication
system using Direct Sequence Spread Spectrum (DSSS), compressive signal
processing can be applied to demodulate the received signal. This may lead to a
decrease in the power consumption or the manufacturing price of wireless
receivers using spread spectrum technology. The main novelty of this paper is
the discovery that in spread spectrum systems it is possible to apply
compressive sensing with a much simpler hardware architecture than in other
systems, making the implementation both simpler and more energy efficient. Our
theoretical work is exemplified with a numerical experiment using the IEEE
802.15.4 standard's 2.4 GHz band specification. The numerical results support
our theoretical findings and indicate that compressive sensing may be used
successfully in spread spectrum communication systems. The results obtained
here may also be applicable in other spread spectrum technologies, such as Code
Division Multiple Access (CDMA) systems.Comment: 5 pages, 2 figures, presented at EUSIPCO 201
Compressive Sensing for Spread Spectrum Receivers
With the advent of ubiquitous computing there are two design parameters of
wireless communication devices that become very important power: efficiency and
production cost. Compressive sensing enables the receiver in such devices to
sample below the Shannon-Nyquist sampling rate, which may lead to a decrease in
the two design parameters. This paper investigates the use of Compressive
Sensing (CS) in a general Code Division Multiple Access (CDMA) receiver. We
show that when using spread spectrum codes in the signal domain, the CS
measurement matrix may be simplified. This measurement scheme, named
Compressive Spread Spectrum (CSS), allows for a simple, effective receiver
design. Furthermore, we numerically evaluate the proposed receiver in terms of
bit error rate under different signal to noise ratio conditions and compare it
with other receiver structures. These numerical experiments show that though
the bit error rate performance is degraded by the subsampling in the CS-enabled
receivers, this may be remedied by including quantization in the receiver
model. We also study the computational complexity of the proposed receiver
design under different sparsity and measurement ratios. Our work shows that it
is possible to subsample a CDMA signal using CSS and that in one example the
CSS receiver outperforms the classical receiver.Comment: 11 pages, 11 figures, 1 table, accepted for publication in IEEE
Transactions on Wireless Communication
Structured Compressed Sensing: From Theory to Applications
Compressed sensing (CS) is an emerging field that has attracted considerable
research interest over the past few years. Previous review articles in CS limit
their scope to standard discrete-to-discrete measurement architectures using
matrices of randomized nature and signal models based on standard sparsity. In
recent years, CS has worked its way into several new application areas. This,
in turn, necessitates a fresh look on many of the basics of CS. The random
matrix measurement operator must be replaced by more structured sensing
architectures that correspond to the characteristics of feasible acquisition
hardware. The standard sparsity prior has to be extended to include a much
richer class of signals and to encode broader data models, including
continuous-time signals. In our overview, the theme is exploiting signal and
measurement structure in compressive sensing. The prime focus is bridging
theory and practice; that is, to pinpoint the potential of structured CS
strategies to emerge from the math to the hardware. Our summary highlights new
directions as well as relations to more traditional CS, with the hope of
serving both as a review to practitioners wanting to join this emerging field,
and as a reference for researchers that attempts to put some of the existing
ideas in perspective of practical applications.Comment: To appear as an overview paper in IEEE Transactions on Signal
Processin
Optical domain subsampling for data-efficient optical coherence tomography (OCT)
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 97-100).Recent advances in optical coherence tomography (OCT) have led to higher-speed sources that support imaging over longer depth ranges. Limitations in the bandwidth of state-of-the-art acquisition electronics, however, prevent adoption of these advances into clinical applications. This thesis introduces optical-domain subsampling as a method for increasing the imaging range while reducing the acquisition bandwidth. Optically subsampled lasers utilize a discrete set of wavelengths to alias fringe signals along an extended depth range into a bandwidth limited window. By detecting the complex fringe signals and under the assumption of a depth-constrained signal, optical domain subsampling enables recovery of the depth-resolved scattering signal without overlapping artifacts. Key principles behind subsampled imaging will be discussed, as well as the design criteria for an experimental subsampled laser. A description of the laser, interferometer, data acquisition system, and signal processing steps is given, and the results of point spread functions compressed into a baseband window are presented. Images that were taken with the subsampled OCT system and a wide-field microscope show that this imaging scheme is viable in vivo and can advantageously image samples that span a long depth range.by Meena Siddiqui.S.M
Reconciling Compressive Sampling Systems for Spectrally-sparse Continuous-time Signals
The Random Demodulator (RD) and the Modulated Wideband Converter (MWC) are
two recently proposed compressed sensing (CS) techniques for the acquisition of
continuous-time spectrally-sparse signals. They extend the standard CS paradigm
from sampling discrete, finite dimensional signals to sampling continuous and
possibly infinite dimensional ones, and thus establish the ability to capture
these signals at sub-Nyquist sampling rates. The RD and the MWC have remarkably
similar structures (similar block diagrams), but their reconstruction
algorithms and signal models strongly differ. To date, few results exist that
compare these systems, and owing to the potential impacts they could have on
spectral estimation in applications like electromagnetic scanning and cognitive
radio, we more fully investigate their relationship in this paper. We show that
the RD and the MWC are both based on the general concept of random filtering,
but employ significantly different sampling functions. We also investigate
system sensitivities (or robustness) to sparse signal model assumptions.
Lastly, we show that "block convolution" is a fundamental aspect of the MWC,
allowing it to successfully sample and reconstruct block-sparse (multiband)
signals. Based on this concept, we propose a new acquisition system for
continuous-time signals whose amplitudes are block sparse. The paper includes
detailed time and frequency domain analyses of the RD and the MWC that differ,
sometimes substantially, from published results.Comment: Corrected typos, updated Section 4.3, 30 pages, 8 figure
A Compressive Phase-Locked Loop
We develop a new method for tracking narrowband signals acquired through compressive sensing, called the compressive sensing phase-locked loop (CS-PLL). The CS-PLL enables one to track oscillating signals in very large bandwidths using a small number of measurements. Not only does the CS-PLL potentially operate below the Nyquist rate, it can extract phase and frequency information without the computational complexity normally associated with compressive sensing signal re-construction. The CS-PLL has a wide variety of applications, including but not limited to communications, phase tracking, robust control, sensing, and FM demodulation. In particular we emphasize the advantages of using this system in wideband surveillence systems. Our design modifies classical PLL designs to operate with CS-based sampling systems. Performance results are shown for PLLs operating on both real and complex data. In addition to explaining general performance tradeoffs, implementations using several different CS sampling systems are explored
Towards Massive Connectivity Support for Scalable mMTC Communications in 5G networks
The fifth generation of cellular communication systems is foreseen to enable
a multitude of new applications and use cases with very different requirements.
A new 5G multiservice air interface needs to enhance broadband performance as
well as provide new levels of reliability, latency and supported number of
users. In this paper we focus on the massive Machine Type Communications (mMTC)
service within a multi-service air interface. Specifically, we present an
overview of different physical and medium access techniques to address the
problem of a massive number of access attempts in mMTC and discuss the protocol
performance of these solutions in a common evaluation framework