1,871 research outputs found
Green compressive sampling reconstruction in IoT networks
In this paper, we address the problem of green Compressed Sensing (CS) reconstruction within Internet of Things (IoT) networks, both in terms of computing architecture and reconstruction algorithms. The approach is novel since, unlike most of the literature dealing with energy efficient gathering of the CS measurements, we focus on the energy efficiency of the signal reconstruction stage given the CS measurements. As a first novel contribution, we present an analysis of the energy consumption within the IoT network under two computing architectures. In the first one, reconstruction takes place within the IoT network and the reconstructed data are encoded and transmitted out of the IoT network; in the second one, all the CS measurements are forwarded to off-network devices for reconstruction and storage, i.e., reconstruction is off-loaded. Our analysis shows that the two architectures significantly differ in terms of consumed energy, and it outlines a theoretically motivated criterion to select a green CS reconstruction computing architecture. Specifically, we present a suitable decision function to determine which architecture outperforms the other in terms of energy efficiency. The presented decision function depends on a few IoT network features, such as the network size, the sink connectivity, and other systems’ parameters. As a second novel contribution, we show how to overcome classical performance comparison of different CS reconstruction algorithms usually carried out w.r.t. the achieved accuracy. Specifically, we consider the consumed energy and analyze the energy vs. accuracy trade-off. The herein presented approach, jointly considering signal processing and IoT network issues, is a relevant contribution for designing green compressive sampling architectures in IoT networks
Graded quantization for multiple description coding of compressive measurements
Compressed sensing (CS) is an emerging paradigm for acquisition of compressed
representations of a sparse signal. Its low complexity is appealing for
resource-constrained scenarios like sensor networks. However, such scenarios
are often coupled with unreliable communication channels and providing robust
transmission of the acquired data to a receiver is an issue. Multiple
description coding (MDC) effectively combats channel losses for systems without
feedback, thus raising the interest in developing MDC methods explicitly
designed for the CS framework, and exploiting its properties. We propose a
method called Graded Quantization (CS-GQ) that leverages the democratic
property of compressive measurements to effectively implement MDC, and we
provide methods to optimize its performance. A novel decoding algorithm based
on the alternating directions method of multipliers is derived to reconstruct
signals from a limited number of received descriptions. Simulations are
performed to assess the performance of CS-GQ against other methods in presence
of packet losses. The proposed method is successful at providing robust coding
of CS measurements and outperforms other schemes for the considered test
metrics
Sparse Signal Processing Concepts for Efficient 5G System Design
As it becomes increasingly apparent that 4G will not be able to meet the
emerging demands of future mobile communication systems, the question what
could make up a 5G system, what are the crucial challenges and what are the key
drivers is part of intensive, ongoing discussions. Partly due to the advent of
compressive sensing, methods that can optimally exploit sparsity in signals
have received tremendous attention in recent years. In this paper we will
describe a variety of scenarios in which signal sparsity arises naturally in 5G
wireless systems. Signal sparsity and the associated rich collection of tools
and algorithms will thus be a viable source for innovation in 5G wireless
system design. We will discribe applications of this sparse signal processing
paradigm in MIMO random access, cloud radio access networks, compressive
channel-source network coding, and embedded security. We will also emphasize
important open problem that may arise in 5G system design, for which sparsity
will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces
Operational Rate-Distortion Performance of Single-source and Distributed Compressed Sensing
We consider correlated and distributed sources without cooperation at the
encoder. For these sources, we derive the best achievable performance in the
rate-distortion sense of any distributed compressed sensing scheme, under the
constraint of high--rate quantization. Moreover, under this model we derive a
closed--form expression of the rate gain achieved by taking into account the
correlation of the sources at the receiver and a closed--form expression of the
average performance of the oracle receiver for independent and joint
reconstruction. Finally, we show experimentally that the exploitation of the
correlation between the sources performs close to optimal and that the only
penalty is due to the missing knowledge of the sparsity support as in (non
distributed) compressed sensing. Even if the derivation is performed in the
large system regime, where signal and system parameters tend to infinity,
numerical results show that the equations match simulations for parameter
values of practical interest.Comment: To appear in IEEE Transactions on Communication
Compressive Classification
This paper derives fundamental limits associated with compressive
classification of Gaussian mixture source models. In particular, we offer an
asymptotic characterization of the behavior of the (upper bound to the)
misclassification probability associated with the optimal Maximum-A-Posteriori
(MAP) classifier that depends on quantities that are dual to the concepts of
diversity gain and coding gain in multi-antenna communications. The diversity,
which is shown to determine the rate at which the probability of
misclassification decays in the low noise regime, is shown to depend on the
geometry of the source, the geometry of the measurement system and their
interplay. The measurement gain, which represents the counterpart of the coding
gain, is also shown to depend on geometrical quantities. It is argued that the
diversity order and the measurement gain also offer an optimization criterion
to perform dictionary learning for compressive classification applications.Comment: 5 pages, 3 figures, submitted to the 2013 IEEE International
Symposium on Information Theory (ISIT 2013
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