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68
On the capacity of large Gaussian relay networks
 IEEE Trans. Inf. Theory
, 2005
"... Abstract—The capacity of a particular large Gaussian relay network is determined in the limit as the number of relays tends to infinity. Upper bounds are derived from cutset arguments, and lower bounds follow from an argument involving uncoded transmission. It is shown that in cases of interest, up ..."
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Cited by 108 (6 self)
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Abstract—The capacity of a particular large Gaussian relay network is determined in the limit as the number of relays tends to infinity. Upper bounds are derived from cutset arguments, and lower bounds follow from an argument involving uncoded transmission. It is shown that in cases of interest, upper and lower bounds coincide in the limit as the number of relays tends to infinity. Hence, this paper provides a new example where a simple cutset upper bound is achievable, and one more example where uncoded transmission achieves optimal performance. The findings are illustrated by geometric interpretations. The techniques developed in this paper are then applied to a sensor network situation. This is a network joint source–channel coding problem, and it is well known that the source–channel separation theorem does not extend to this case. The present paper extends this insight by providing an example where separating source from channel coding does not only lead to suboptimal performance—it leads to an exponential penalty in performance scaling behavior (as a function of the number of nodes). Finally, the techniques developed in this paper are extended to include certain models of ad hoc wireless networks, where a capacity scaling law can be established: When all nodes act purely as relays for a single source–destination pair, capacity grows with the logarithm of the number of nodes. Index Terms—Capacity, CEO problem, joint source–channel coding, network, relay, sensor network, separation theorem, uncoded transmission. I.
Lossy Source Coding
 IEEE Trans. Inform. Theory
, 1998
"... Lossy coding of speech, highquality audio, still images, and video is commonplace today. However, in 1948, few lossy compression systems were in service. Shannon introduced and developed the theory of source coding with a fidelity criterion, also called ratedistortion theory. For the first 25 year ..."
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Cited by 71 (1 self)
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Lossy coding of speech, highquality audio, still images, and video is commonplace today. However, in 1948, few lossy compression systems were in service. Shannon introduced and developed the theory of source coding with a fidelity criterion, also called ratedistortion theory. For the first 25 years of its existence, ratedistortion theory had relatively little impact on the methods and systems actually used to compress real sources. Today, however, ratedistortion theoretic concepts are an important component of many lossy compression techniques and standards. We chronicle the development of ratedistortion theory and provide an overview of its influence on the practice of lossy source coding. Index TermsData compression, image coding, speech coding, rate distortion theory, signal coding, source coding with a fidelity criterion, video coding. I.
Sourcechannel communication in sensor networks
 Lecture Notes in Computer Science
, 2003
"... Abstract. Sensors acquire data, and communicate this to an interested party. The arising coding problem is often split into two parts: First, the sensors compress their respective acquired signals, potentially applying the concepts of distributed source coding. Then, they communicate the compressed ..."
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Cited by 60 (11 self)
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Abstract. Sensors acquire data, and communicate this to an interested party. The arising coding problem is often split into two parts: First, the sensors compress their respective acquired signals, potentially applying the concepts of distributed source coding. Then, they communicate the compressed version to the interested party, the goal being not to make any errors. This coding paradigm is inspired by Shannon’s separation theorem for pointtopoint communication, but it leads to suboptimal performance in general network topologies. The optimal performance for the general case is not known. In this paper, we propose an alternative coding paradigm based on joint sourcechannel coding. This coding paradigm permits to determine the optimal performance for a class of sensor networks, and shows how to achieve it. For sensor networks outside this class, we argue that the goal of the coding system could be to approach our condition for optimal performance as closely as possible. This is supported by examples for which our coding paradigm significantly outperforms the traditional separationbased coding paradigm. In particular, for a Gaussian example considered in this paper, the distortion of the best coding scheme according to the separation paradigm decreases like 1 / log M, while for our coding paradigm, it decreases like 1/M, where M is the total number of sensors. 1
Distributed compressionestimation using wireless sensor networks  The design goals of performance, bandwidth efficiency, scalability, and robustness
 IEEE SIGNAL PROCESSING MAG
, 2006
"... A wireless sensor network (WSN) consists of a large number of spatially distributed signal processing devices (nodes), each with finite battery lifetime and thus limited computing and communication capabilities. When properly programmed and networked, nodes in a WSN can cooperate to perform advance ..."
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Cited by 32 (1 self)
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A wireless sensor network (WSN) consists of a large number of spatially distributed signal processing devices (nodes), each with finite battery lifetime and thus limited computing and communication capabilities. When properly programmed and networked, nodes in a WSN can cooperate to perform advanced signal processing tasks with unprecedented robustness and versatility, thus making WSN an attractive lowcost technology for a wide range of remote sensing and environmental monitoring applications [1], [32].
The Rate Region of the Quadratic Gaussian TwoTerminal SourceCoding Problem. Submitted for publication. Available from http://www.arxiv.org/abs/cs.IT/0510095
"... We consider a problem in which two encoders each observe one component of a memoryless Gaussian vectorvalued source. The encoders separately communicate with a decoder, which attempts to reproduce the vectorvalued source subject to constraints on the expected squared error of each component. We de ..."
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Cited by 31 (2 self)
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We consider a problem in which two encoders each observe one component of a memoryless Gaussian vectorvalued source. The encoders separately communicate with a decoder, which attempts to reproduce the vectorvalued source subject to constraints on the expected squared error of each component. We determine the minimum sum rate needed to meet a pair of target distortions and thereby complete the determination of the rate region for this problem. The proof involves coupling the problem to a quadratic Gaussian “CEO problem.” 1
Rate region of the quadratic Gaussian twoencoder sourcecoding problem
 IEEE Trans. Inf. Theory
, 2008
"... We determine the rate region of the quadratic Gaussian twoencoder sourcecoding problem. This rate region is achieved by a simple architecture that separates the analog and digital aspects of the compression. Furthermore, this architecture requires higher rates to send a Gaussian source than it doe ..."
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Cited by 30 (2 self)
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We determine the rate region of the quadratic Gaussian twoencoder sourcecoding problem. This rate region is achieved by a simple architecture that separates the analog and digital aspects of the compression. Furthermore, this architecture requires higher rates to send a Gaussian source than it does to send any other source with the same covariance. Our techniques can also be used to determine the sum rate of some generalizations of this classical problem. Our approach involves coupling the problem to a quadratic Gaussian “CEO problem.”
Uncoded transmission is exactly optimal for a simple Gaussian sensor network
 in Proc. 2007 ITA Workshop
, 2007
"... Abstract — One of the simplest sensor network models has one single underlying Gaussian source of interest, observed by many sensors, subject to independent Gaussian observation noise. The sensors communicate over a standard Gaussian multipleaccess channel to a fusion center whose goal is to estimat ..."
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Cited by 30 (2 self)
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Abstract — One of the simplest sensor network models has one single underlying Gaussian source of interest, observed by many sensors, subject to independent Gaussian observation noise. The sensors communicate over a standard Gaussian multipleaccess channel to a fusion center whose goal is to estimate the underlying source with respect to meansquared error. In this note, a theorem of Witsenhausen is shown to imply that an optimal communication strategy is uncoded transmission, i.e., each sensors ’ channel input is merely a scaled version of its noisy observation. I.
Sequential Signal Encoding from Noisy Measurements Using Quantizers with Dynamic Bias Control
 IEEE Transactions on Information Theory
, 2001
"... Signal estimation from a sequential encoding in the form of quantized noisy measurements is considered. As an example context, this problem arises in a number of remote sensing applications, where a central site estimates an informationbearing signal from lowbandwidth digitized information receive ..."
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Cited by 28 (1 self)
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Signal estimation from a sequential encoding in the form of quantized noisy measurements is considered. As an example context, this problem arises in a number of remote sensing applications, where a central site estimates an informationbearing signal from lowbandwidth digitized information received from remote sensors, and may or may not broadcast feedback information to the sensors. We demonstrate that the use of an appropriately designed and often easily implemented additive control input before signal quantization at the sensor can significantly enhance overall system performance. In particular, we develop efficient estimators in conjunction with optimized random, deterministic, and feedbackbased control inputs, resulting in a hierarchy of systems that trade performance for complexity.
Distributed detection in sensor networks with packet losses and finite capacity links
 IEEE Transactions on Signal Processing
, 2006
"... We consider a multiobject detection problem over a sensor network (SNET) with limited range multimodal sensors. Limited range sensing environment arises in a sensing field prone to signal attenuation and path losses. The general problem complements the widely considered decentralized detection pro ..."
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Cited by 28 (1 self)
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We consider a multiobject detection problem over a sensor network (SNET) with limited range multimodal sensors. Limited range sensing environment arises in a sensing field prone to signal attenuation and path losses. The general problem complements the widely considered decentralized detection problem where all sensors observe the same object. In this paper we develop a distributed detection approach based on recent development of the false discovery rate (FDR) and the associated BH test procedure. The BH procedure is based on rank ordering of scalar test statistics. We first develop scalar test statistics for multidimensional data to handle multimodal sensor observations and establish its optimality in terms of the BH procedure. We then propose a distributed algorithm in the ideal case of infinite attenuation for identification of sensors that are in the immediate vicinity of an object. We demonstrate communication message scalability to large SNETs by showing that the upper bound on the communication message complexity scales linearly with the number of sensors that are in the vicinity of objects and is independent of the total number of sensors in the SNET. This brings forth an important principle for evaluating the performance of an SNET, namely, the need for scalability of communications and performance with respect to the number of objects or events in an SNET irrespective of the network size. We then account for finite attenuation by modeling sensor observations as corrupted by uncertain interference arising from distant objects and developing robust extensions to our idealized distributed scheme. The robustness properties ensure that both the error performance and communication message complexity degrade gracefully with interference. 1