Results 1  10
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70
Signal reconstruction from noisy random projections
 IEEE Trans. Inform. Theory
, 2006
"... Recent results show that a relatively small number of random projections of a signal can contain most of its salient information. It follows that if a signal is compressible in some orthonormal basis, then a very accurate reconstruction can be obtained from random projections. We extend this type of ..."
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Cited by 168 (21 self)
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Recent results show that a relatively small number of random projections of a signal can contain most of its salient information. It follows that if a signal is compressible in some orthonormal basis, then a very accurate reconstruction can be obtained from random projections. We extend this type of result to show that compressible signals can be accurately recovered from random projections contaminated with noise. We also propose a practical iterative algorithm for signal reconstruction, and briefly discuss potential applications to coding, A/D conversion, and remote wireless sensing. Index Terms sampling, signal reconstruction, random projections, denoising, wireless sensor networks
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.
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
Stochastic Linear Control over a Communication Channel
, 2003
"... We examine linear stochastic control systems when there is a communication channel connecting the sensor to the controller. The problem consists of designing the channel encoder and decoder as well as the controller to satisfy some given control objectives. In particular we examine the role communic ..."
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Cited by 52 (8 self)
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We examine linear stochastic control systems when there is a communication channel connecting the sensor to the controller. The problem consists of designing the channel encoder and decoder as well as the controller to satisfy some given control objectives. In particular we examine the role communication has on the classical LQG problem. We give conditions under which the classical separation property between estimation and control holds and the certainty equivalent control law is optimal. We then present the sequential rate distortion framework. We present bounds on the achievable performance and show the inherent tradeo#s between control and communication costs. In particular we show that optimal quadratic cost decomposes into two terms: a full knowledge cost and a sequential rate distortion cost.
Fast transfer of channel state information in wireless systems
 IEEE Transactions on Signal Processing
, 2006
"... Knowledge of accurate and timely channel state information (CSI) at the transmitter is becoming increasingly important in wireless communication systems. While it is often assumed that the receiver (whether basestation or mobile) needs to know the channel for accurate power control, scheduling and d ..."
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Cited by 38 (1 self)
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Knowledge of accurate and timely channel state information (CSI) at the transmitter is becoming increasingly important in wireless communication systems. While it is often assumed that the receiver (whether basestation or mobile) needs to know the channel for accurate power control, scheduling and datademodulation, it is now known that the transmitter (especially the basestation) can also benefit greatly from this information. For example, recent results in multiantenna multiuser systems show that large throughput gains are possible when the basestation uses multiple antennas and a known channel to transmit distinct messages simultaneously and selectively to many singleantenna users. In timedivision duplex systems, where the basestation and mobiles share the same frequency band for transmission, the basestation can exploit reciprocity to obtain the forward channel from pilots received over the reverse channel. Frequencydivision duplex systems are more difficult because the basestation transmits and receives on different frequencies and therefore cannot use the received pilot to infer anything about the multiantenna transmit channel. Nevertheless, we show that the time occupied in frequencyduplex CSI transfer is generally less than one might expect, and falls as the number of antennas increases. Thus, although the total amount of channel information increases with the number of antennas at the basestation,
Optimal distributed detection strategies for wireless sensor networks
 in 42nd Annual Allerton Conf. on Commun., Control and Comp
, 2004
"... We study optimal distributed detection strategies for wireless sensor networks under the assumption of spatially and temporally i.i.d. observations at the sensor nodes. Each node computes a local statistic and communicates it to a decision center over a noisy channel. The performance of centralized ..."
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Cited by 35 (3 self)
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We study optimal distributed detection strategies for wireless sensor networks under the assumption of spatially and temporally i.i.d. observations at the sensor nodes. Each node computes a local statistic and communicates it to a decision center over a noisy channel. The performance of centralized detection (noisefree channel) serves as a benchmark. We address the following fundamental question: under what network resource constraints can distributed detection achieve the same error exponent as centralized detection? Two types of constraints are considered: 1) transmission power constraints at the nodes, and 2) the communication channel between the nodes and the decision center. Two types of channels are studied: 1) a parallel access channel (PAC) consisting of dedicated AWGN channels between the nodes and the decision center, and 2) an AWGN multiple access channel (MAC). We show that for intelligent sensors (with knowledge of observation statistics) analog communication of local likelihood ratios (soft decisions) over the MAC is asymptotically optimal (for large number of nodes) when each node can communicate with a constant power. Motivated by this result, we propose an optimal distributed detection strategy for dumb sensors (oblivious of observation statistics) based on the method of types. In this strategy, each node appropriately quantizes its temporal observation data and communicates its type or histogram to the decision center. It is shown that typebased distributed detection over the MAC is also asymptotically optimal with an additional advantage: observation statistics are needed only at the decision center. Even under the more stringet total power constraint, it is shown that both soft decision and typefusion result in exponentially decaying error probability. 1
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.
Sending a Bivariate Gaussian Source over a Gaussian MAC
 in Proceedings IEEE International Symposium on Information Theory
"... We study the powerversusdistortion tradeoff for the transmission of a memoryless bivariate Gaussian source over a twotoone Gaussian multipleaccess channel with perfect causal feedback. In this problem, each of two separate transmitters observes a different component of a memoryless bivariate G ..."
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Cited by 24 (3 self)
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We study the powerversusdistortion tradeoff for the transmission of a memoryless bivariate Gaussian source over a twotoone Gaussian multipleaccess channel with perfect causal feedback. In this problem, each of two separate transmitters observes a different component of a memoryless bivariate Gaussian source as well as the feedback from the channel output of the previous timeinstants. Based on the observed source sequence and the feedback, each transmitter then describes its source component to the common receiver via an averagepower constrained Gaussian multipleaccess channel. From the resulting channel output, the receiver wishes to reconstruct both source components with the least possible expected squarederror distortion. We study the set of distortion pairs that can be achieved by the receiver on the two source components. We present sufficient conditions and necessary conditions for the achievability of a distortion pair. These conditions are expressed in terms of the source correlation and of the signaltonoise ratio (SNR) of the channel. In several cases the necessary conditions and sufficient conditions coincide. This allows us to show that if the channel SNR is below a certain threshold, then an uncoded transmission scheme that ignores the feedback is optimal. Thus, below this SNRthreshold feedback is useless. We also derive the precise highSNR asymptotics of optimal schemes. 1
Joint sourcechannel coding error exponent for discrete communication systems with Markovian memory
 IEEE Trans. Info. Theory
, 2007
"... Abstract—We investigate the computation of Csiszár’s bounds for the joint source–channel coding (JSCC) error exponent of a communication system consisting of a discrete memoryless source and a discrete memoryless channel. We provide equivalent expressions for these bounds and derive explicit formula ..."
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Cited by 23 (9 self)
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Abstract—We investigate the computation of Csiszár’s bounds for the joint source–channel coding (JSCC) error exponent of a communication system consisting of a discrete memoryless source and a discrete memoryless channel. We provide equivalent expressions for these bounds and derive explicit formulas for the rates where the bounds are attained. These equivalent representations can be readily computed for arbitrary source–channel pairs via Arimoto’s algorithm. When the channel’s distribution satisfies a symmetry property, the bounds admit closedform parametric expressions. We then use our results to provide a systematic comparison between the JSCC error exponent and the tandem coding error exponent, which applies if the source and channel are separately coded. It is shown that 2. We establish conditions for which and for which =2. Numerical examples indicate that is close to2 for many source– channel pairs. This gain translates into a power saving larger than 2 dB for a binary source transmitted over additive white Gaussian noise (AWGN) channels and Rayleighfading channels with finite output quantization. Finally, we study the computation of the lossy JSCC error exponent under the Hamming distortion measure. Index Terms—Discrete memoryless sources and channels, error exponent, Fenchel’s duality, Hamming distortion measure, joint source–channel coding, randomcoding exponent, reliability function, spherepacking exponent, symmetric channels, tandem source and channel coding. I.
Multiuser MIMO Downlink Made Practical: Achievable Rates with Simple Channel State Estimation and Feedback Schemes
, 2007
"... We consider a MIMO fading broadcast channel and compute achievable ergodic rates when channel state information is acquired at the receivers via downlink training and explicit channel feedback is performed to provide transmitter channel state information (CSIT). Both “analog” and quantized (digital) ..."
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Cited by 17 (5 self)
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We consider a MIMO fading broadcast channel and compute achievable ergodic rates when channel state information is acquired at the receivers via downlink training and explicit channel feedback is performed to provide transmitter channel state information (CSIT). Both “analog” and quantized (digital) channel feedback are analyzed, and digital feedback is shown to be potentially superior when the feedback channel uses per channel coefficient is larger than 1. Also, we show that by proper design of the digital feedback link, errors in the feedback have a relatively minor effect even if simple uncoded modulation is used on the feedback channel. We extend our analysis to the case of fading MIMO Multiaccess Channel (MIMOMAC) in the feedback link, as well as to the case of a timevarying channel and feedback delay. We show that by exploiting the MIMOMAC nature of the uplink channel, a fully scalable system with both downlink multiplexing gain and feedback redundancy proportional to the number of base station antennas can be achieved. Furthermore, the feedback strategy is optimized by a nontrivial combination of timedivision and spacedivision multipleaccess. For the case of delayed feedback, we show that in the realistic case where the fading process has (normalized) maximum Doppler frequency shift 0 ≤ F < 1/2, a fraction 1 − 2F of the optimal multiplexing gain is achievable. The general conclusion of this work is that very significant downlink throughput is achievable with simple and efficient channel state feedback, provided that the feedback link is properly designed.