2,371 research outputs found
Combinatorial Channel Signature Modulation for Wireless ad-hoc Networks
In this paper we introduce a novel modulation and multiplexing method which
facilitates highly efficient and simultaneous communication between multiple
terminals in wireless ad-hoc networks. We term this method Combinatorial
Channel Signature Modulation (CCSM). The CCSM method is particularly efficient
in situations where communicating nodes operate in highly time dispersive
environments. This is all achieved with a minimal MAC layer overhead, since all
users are allowed to transmit and receive at the same time/frequency (full
simultaneous duplex). The CCSM method has its roots in sparse modelling and the
receiver is based on compressive sampling techniques. Towards this end, we
develop a new low complexity algorithm termed Group Subspace Pursuit. Our
analysis suggests that CCSM at least doubles the throughput when compared to
the state-of-the art.Comment: 6 pages, 7 figures, to appear in IEEE International Conference on
Communications ICC 201
Compressed Sensing and Parallel Acquisition
Parallel acquisition systems arise in various applications in order to
moderate problems caused by insufficient measurements in single-sensor systems.
These systems allow simultaneous data acquisition in multiple sensors, thus
alleviating such problems by providing more overall measurements. In this work
we consider the combination of compressed sensing with parallel acquisition. We
establish the theoretical improvements of such systems by providing recovery
guarantees for which, subject to appropriate conditions, the number of
measurements required per sensor decreases linearly with the total number of
sensors. Throughout, we consider two different sampling scenarios -- distinct
(corresponding to independent sampling in each sensor) and identical
(corresponding to dependent sampling between sensors) -- and a general
mathematical framework that allows for a wide range of sensing matrices (e.g.,
subgaussian random matrices, subsampled isometries, random convolutions and
random Toeplitz matrices). We also consider not just the standard sparse signal
model, but also the so-called sparse in levels signal model. This model
includes both sparse and distributed signals and clustered sparse signals. As
our results show, optimal recovery guarantees for both distinct and identical
sampling are possible under much broader conditions on the so-called sensor
profile matrices (which characterize environmental conditions between a source
and the sensors) for the sparse in levels model than for the sparse model. To
verify our recovery guarantees we provide numerical results showing phase
transitions for a number of different multi-sensor environments.Comment: 43 pages, 4 figure
Measuring Cerebral Activation From fNIRS Signals: An Approach Based on Compressive Sensing and Taylor-Fourier Model
Functional near-infrared spectroscopy (fNIRS) is a noninvasive and portable neuroimaging technique that uses NIR light to monitor cerebral activity by the so-called haemodynamic responses (HRs). The measurement is challenging because of the presence of severe physiological noise, such as respiratory and vasomotor waves. In this paper, a novel technique for fNIRS signal denoising and HR estimation is described. The method relies on a joint application of compressed sensing theory principles and Taylor-Fourier modeling of nonstationary spectral components. It operates in the frequency domain and models physiological noise as a linear combination of sinusoidal tones, characterized in terms of frequency, amplitude, and initial phase. Algorithm performance is assessed over both synthetic and experimental data sets, and compared with that of two reference techniques from fNIRS literature
Towards sparse characterisation of on-body ultra-wideband wireless channels
With the aim of reducing cost and power consumption of the receiving terminal, compressive sensing (CS) framework is applied to on-body ultra-wideband (UWB) channel estimation. It is demonstrated in this Letter that the sparse on-body UWB channel impulse response recovered by the CS framework fits the original sparse channel well; thus, on-body channel estimation can be achieved using low-speed sampling devices
Tensor Decompositions for Signal Processing Applications From Two-way to Multiway Component Analysis
The widespread use of multi-sensor technology and the emergence of big
datasets has highlighted the limitations of standard flat-view matrix models
and the necessity to move towards more versatile data analysis tools. We show
that higher-order tensors (i.e., multiway arrays) enable such a fundamental
paradigm shift towards models that are essentially polynomial and whose
uniqueness, unlike the matrix methods, is guaranteed under verymild and natural
conditions. Benefiting fromthe power ofmultilinear algebra as theirmathematical
backbone, data analysis techniques using tensor decompositions are shown to
have great flexibility in the choice of constraints that match data properties,
and to find more general latent components in the data than matrix-based
methods. A comprehensive introduction to tensor decompositions is provided from
a signal processing perspective, starting from the algebraic foundations, via
basic Canonical Polyadic and Tucker models, through to advanced cause-effect
and multi-view data analysis schemes. We show that tensor decompositions enable
natural generalizations of some commonly used signal processing paradigms, such
as canonical correlation and subspace techniques, signal separation, linear
regression, feature extraction and classification. We also cover computational
aspects, and point out how ideas from compressed sensing and scientific
computing may be used for addressing the otherwise unmanageable storage and
manipulation problems associated with big datasets. The concepts are supported
by illustrative real world case studies illuminating the benefits of the tensor
framework, as efficient and promising tools for modern signal processing, data
analysis and machine learning applications; these benefits also extend to
vector/matrix data through tensorization. Keywords: ICA, NMF, CPD, Tucker
decomposition, HOSVD, tensor networks, Tensor Train
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