Fundamental Tradeoffs between Sparsity, Sensing Diversity and Sensing Capacity

Aeron, Shuchin; Zhao, Manqi; Saligrama, Venkatesh

doi:10.1109/acssc.2006.356635

Cited by 8 publications

(13 citation statements)

References 7 publications

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“…Another issue to be noted with respect to the sensing capacity is the inherent difference between sensor network and CS scenarios in the way in which the SNR is handled [14,172]. In sensor networks composed of many sensors, fixed SNR can be imposed for each individual sensor.…”

Section: Sensing Capacitymentioning

confidence: 99%

“…The inverse of sensing capacity is the compression rate; i.e., the ratio of the number of measurements to the number of signal dimensions which characterizes the minimum rate to which the source can be compressed. As shown in [14], sensing capacity is a function of SNR, the inherent dimensionality of the information space, sensing diversity, and the desired distortion level.…”

Section: Sensing Capacitymentioning

confidence: 99%

“…One of the issues that has recently attracted attention in theoretical analysis of sensor networks is the concept of sensor capacity. The sensing capacity was initially introduced for discrete alphabets in applications such as target detection [170] and later extended in [14,171,172] to the continuous case. The questions in this area are related to the problem of sampling of sparse signals, [29,76,159] and sampling with finite rate of innovation [3,103].…”

Section: Sensing Capacitymentioning

confidence: 99%

“…In sensor networks (row 13), both deterministic and stochastic signal models are employed. Finally, in SCA (rows [14][15], statistical independence of sources may be necessary and thus stochastic models are applied.…”

Section: Introductionmentioning

confidence: 99%

“…It is the intention of this tutorial to describe these methods in each field succinctly and show that these methods can be used in other areas and applications often with appreciable improvements. Among these fields are 1-Sampling: random sampling of bandlimited signals [1], compressed sensing (CS) [2], and sampling with finite rate of innovation [3]; 2-Coding: Galois [4,5] and real-field error correction http://asp.eurasipjournals.com/content/2012/1/44 codes [6]; 3-Spectral Estimation [7][8][9][10]; 4-Array Processing: Multi-source location (MSL) and direction of arrival (DOA) estimation [11,12], sparse array processing [13], and sensor networks [14]; 5-Sparse Component Analysis (SCA): blind source separation [15][16][17] and dictionary representation [18][19][20]; 6-Channel Estimation in Orthogonal Frequency Division Multiplexing (OFDM) [21][22][23]. The sparsity properties of these fields are summarized in Tables 1, 2, and 3. b The details of most of the major applications will be discussed in the next sections but the common traits will be discussed in this introduction.…”

Section: Introductionmentioning

confidence: 99%

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A unified approach to sparse signal processing

Marvasti

Amini

Haddadi

et al. 2012

EURASIP J. Adv. Signal Process.

101

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A unified view of the area of sparse signal processing is presented in tutorial form by bringing together various fields in which the property of sparsity has been successfully exploited. For each of these fields, various algorithms and techniques, which have been developed to leverage sparsity, are described succinctly. The common potential benefits of significant reduction in sampling rate and processing manipulations through sparse signal processing are revealed. The key application domains of sparse signal processing are sampling, coding, spectral estimation, array processing, component analysis, and multipath channel estimation. In terms of the sampling process and reconstruction algorithms, linkages are made with random sampling, compressed sensing, and rate of innovation. The redundancy introduced by channel coding in finite and real Galois fields is then related to over-sampling with similar reconstruction algorithms. The error locator polynomial (ELP) and iterative methods are shown to work quite effectively for both sampling and coding applications. The methods of Prony, Pisarenko, and MUltiple SIgnal Classification (MUSIC) are next shown to be targeted at analyzing signals with sparse frequency domain representations. Specifically, the relations of the approach of Prony to an annihilating filter in rate of innovation and ELP in coding are emphasized; the Pisarenko and MUSIC methods are further improvements of the Prony method under noisy environments. The iterative methods developed for sampling and coding applications are shown to be powerful tools in spectral estimation. Such narrowband spectral estimation is then related to multi-source location and direction of arrival estimation in array processing. Sparsity in unobservable source signals is also shown to facilitate source separation in sparse component analysis; the algorithms developed in this area such as linear programming and matching pursuit are also widely used in compressed sensing. Finally, the multipath channel estimation problem is shown to have a sparse formulation; algorithms similar to sampling and coding are used to estimate typical multicarrier communication channels.

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Section: Sensing Capacitymentioning

confidence: 99%

Section: Sensing Capacitymentioning

confidence: 99%