The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform
Chao Tian - One of the best experts on this subject based on the ideXlab platform.
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Matched Multiuser Gaussian Source Channel Communications via Uncoded Schemes
IEEE Transactions on Information Theory, 2017Co-Authors: Chao Tian, Suhas N. Diggavi, J. Chen, Shlomo Shamai ShitzAbstract:We investigate whether uncoded schemes are optimal for Gaussian sources on multiuser Gaussian channels. Particularly, we consider two problems: the first is to send correlated Gaussian sources on a Gaussian broadcast channel where each receiver is interested in reconstructing only one source component (or one specific linear function of the sources) under the mean Squared Error Distortion measure; the second is to send correlated Gaussian sources on a Gaussian multiple-access channel, where each transmitter observes a noisy combination of the sources, and the receiver wishes to reconstruct the individual source components (or individual linear functions) under the mean Squared Error Distortion measure. It is shown that when the channel parameters satisfy certain general conditions, the induced Distortion tuples are on the boundary of the achievable Distortion region, and thus optimal. Instead of following the conventional approach of attempting to characterize the achievable Distortion region, we ask the question whether and how a match can be effectively determined. This decision problem formulation helps to circumvent the difficult optimization problem often embedded in region characterization problems, and it also leads us to focus on the critical conditions in the outer bounds that make the inequalities become equalities, which effectively decouple the overall problem into several simpler sub-problems. Optimality results previously unknown in the literature are obtained using this novel approach. Explicit and novel outer bounds are derived for the two problems as the byproducts of our investigation.
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ISIT - Matched multiuser Gaussian source-channel communications via uncoded schemes
2015 IEEE International Symposium on Information Theory (ISIT), 2015Co-Authors: Chao Tian, J. Chen, Suhas Diggavi, Shlomo ShamaiAbstract:We investigate whether uncoded schemes are optimal for Gaussian sources on multiuser Gaussian channels. Particularly, we consider two problems: the first is to send correlated Gaussian sources on a Gaussian broadcast channel where each receiver is interested in reconstructing only one source component (or one specific linear function of the sources) under the mean Squared Error Distortion measure; the second is to send correlated Gaussian sources on a Gaussian multiple-access channel, where each transmitter observes a noisy combination of the source, and the receiver wishes to reconstruct the individual source components (or individual linear functions) under the mean Squared Error Distortion measure. It is shown that when the channel parameters match certain general conditions, the induced Distortion tuples are on the boundary of the achievable Distortion region, and thus optimal. Instead of following the conventional approach of attempting to characterize the achievable Distortion region, we ask the question whether and how a match can be effectively determined. This decision problem formulation helps to circumvent the difficult optimization problem often embedded in region characterization problems, and it also leads us to focus on the critical conditions in the outer bounds that make the inequalities become equalities, which effectively decouples the overall problem into several simpler sub-problems.
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Broadcasting Correlated Vector Gaussians
IEEE Transactions on Information Theory, 2015Co-Authors: Lin Song, J. Chen, Chao TianAbstract:The problem of sending two correlated vector Gaussian sources over a bandwidth-matched two-user scalar Gaussian broadcast channel is studied in this paper, where each receiver wishes to reconstruct its target source under a covariance Distortion constraint. We derive a lower bound on the optimal tradeoff between the transmit power and the achievable reconstruction Distortion pair. Our derivation is based on a new bounding technique which involves the introduction of appropriate remote sources. Furthermore, it is shown that this lower bound is achievable by a class of hybrid schemes for the special case, where the weak receiver wishes to reconstruct a scalar source under the mean Squared Error Distortion constraint.
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Approximating the Gaussian Multiple Description Rate Region Under Symmetric Distortion Constraints
IEEE Transactions on Information Theory, 2009Co-Authors: Chao Tian, Soheil Mohajer, Suhas DiggaviAbstract:We consider multiple description coding for the Gaussian source with K descriptions under the symmetric mean Squared Error Distortion constraints, and provide an approximate characterization of the rate region. We show that the rate region can be sandwiched between two polytopes, between which the gap can be upper bounded by constants dependent on the number of descriptions, but independent of the exact Distortion constraints. Underlying this result is an exact characterization of the lossless multi-level diversity source coding problem: a lossless counterpart of the MD problem. This connection provides a polytopic template for the inner and outer bounds to the rate region. In order to establish the outer bound, we generalize Ozarow's technique to introduce a strategic expansion of the original probability space by more than one random variables. For the symmetric rate case with any number of descriptions, we show that the gap between the upper bound and the lower bound for the individual description rate is no larger than 0.92 bit. The results developed in this work also suggest the "separation" approach of combining successive refinement quantization and lossless multi-level diversity coding is a competitive one, since it is only a constant away from the optimum. The results are further extended to general sources under the mean Squared Error Distortion measure, where a similar but looser bound on the gap holds.
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Multiple Description Coding for Stationary Gaussian Sources
IEEE Transactions on Information Theory, 2009Co-Authors: J. Chen, Chao Tian, Suhas DiggaviAbstract:We consider the problem of multiple description coding for stationary Gaussian sources under the Squared Error Distortion measure. The rate region is characterized for the 2-description case. It is shown that each supporting line of the rate region is achievable with a transform lattice quantization scheme. We show the optimal coding scheme has a natural spectral domain coding interpretation, which yields a reverse water-filling solution with a frequency-dependent water level instead of the flat water level as in the conventional single description case.
J. Chen - One of the best experts on this subject based on the ideXlab platform.
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Asymptotic Rate-Distortion Analysis of Symmetric Remote Gaussian Source Coding: Centralized Encoding vs. Distributed Encoding.
Entropy (Basel Switzerland), 2019Co-Authors: Yizhong Wang, Li Xie, Siyao Zhou, Mengzhen Wang, J. ChenAbstract:Consider a symmetric multivariate Gaussian source with l components, which are corrupted by independent and identically distributed Gaussian noises; these noisy components are compressed at a certain rate, and the compressed version is leveraged to reconstruct the source subject to a mean Squared Error Distortion constraint. The rate-Distortion analysis is performed for two scenarios: centralized encoding (where the noisy source components are jointly compressed) and distributed encoding (where the noisy source components are separately compressed). It is shown, among other things, that the gap between the rate-Distortion functions associated with these two scenarios admits a simple characterization in the large l limit.
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Matched Multiuser Gaussian Source Channel Communications via Uncoded Schemes
IEEE Transactions on Information Theory, 2017Co-Authors: Chao Tian, Suhas N. Diggavi, J. Chen, Shlomo Shamai ShitzAbstract:We investigate whether uncoded schemes are optimal for Gaussian sources on multiuser Gaussian channels. Particularly, we consider two problems: the first is to send correlated Gaussian sources on a Gaussian broadcast channel where each receiver is interested in reconstructing only one source component (or one specific linear function of the sources) under the mean Squared Error Distortion measure; the second is to send correlated Gaussian sources on a Gaussian multiple-access channel, where each transmitter observes a noisy combination of the sources, and the receiver wishes to reconstruct the individual source components (or individual linear functions) under the mean Squared Error Distortion measure. It is shown that when the channel parameters satisfy certain general conditions, the induced Distortion tuples are on the boundary of the achievable Distortion region, and thus optimal. Instead of following the conventional approach of attempting to characterize the achievable Distortion region, we ask the question whether and how a match can be effectively determined. This decision problem formulation helps to circumvent the difficult optimization problem often embedded in region characterization problems, and it also leads us to focus on the critical conditions in the outer bounds that make the inequalities become equalities, which effectively decouple the overall problem into several simpler sub-problems. Optimality results previously unknown in the literature are obtained using this novel approach. Explicit and novel outer bounds are derived for the two problems as the byproducts of our investigation.
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ISIT - Matched multiuser Gaussian source-channel communications via uncoded schemes
2015 IEEE International Symposium on Information Theory (ISIT), 2015Co-Authors: Chao Tian, J. Chen, Suhas Diggavi, Shlomo ShamaiAbstract:We investigate whether uncoded schemes are optimal for Gaussian sources on multiuser Gaussian channels. Particularly, we consider two problems: the first is to send correlated Gaussian sources on a Gaussian broadcast channel where each receiver is interested in reconstructing only one source component (or one specific linear function of the sources) under the mean Squared Error Distortion measure; the second is to send correlated Gaussian sources on a Gaussian multiple-access channel, where each transmitter observes a noisy combination of the source, and the receiver wishes to reconstruct the individual source components (or individual linear functions) under the mean Squared Error Distortion measure. It is shown that when the channel parameters match certain general conditions, the induced Distortion tuples are on the boundary of the achievable Distortion region, and thus optimal. Instead of following the conventional approach of attempting to characterize the achievable Distortion region, we ask the question whether and how a match can be effectively determined. This decision problem formulation helps to circumvent the difficult optimization problem often embedded in region characterization problems, and it also leads us to focus on the critical conditions in the outer bounds that make the inequalities become equalities, which effectively decouples the overall problem into several simpler sub-problems.
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Broadcasting Correlated Vector Gaussians
IEEE Transactions on Information Theory, 2015Co-Authors: Lin Song, J. Chen, Chao TianAbstract:The problem of sending two correlated vector Gaussian sources over a bandwidth-matched two-user scalar Gaussian broadcast channel is studied in this paper, where each receiver wishes to reconstruct its target source under a covariance Distortion constraint. We derive a lower bound on the optimal tradeoff between the transmit power and the achievable reconstruction Distortion pair. Our derivation is based on a new bounding technique which involves the introduction of appropriate remote sources. Furthermore, it is shown that this lower bound is achievable by a class of hybrid schemes for the special case, where the weak receiver wishes to reconstruct a scalar source under the mean Squared Error Distortion constraint.
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Gaussian Robust Sequential and Predictive Coding
IEEE Transactions on Information Theory, 2013Co-Authors: Lin Song, J. Chen, Jia Wang, Tie LiuAbstract:We introduce two new source coding problems: robust sequential coding and robust predictive coding. For the Gauss-Markov source model with the mean Squared Error Distortion measure, we characterize certain supporting hyperplanes of the rate region of these two coding problems. Our investigation also reveals an information-theoretic minimax theorem and the associated extremal inequalities.
Suhas Diggavi - One of the best experts on this subject based on the ideXlab platform.
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ISIT - Matched multiuser Gaussian source-channel communications via uncoded schemes
2015 IEEE International Symposium on Information Theory (ISIT), 2015Co-Authors: Chao Tian, J. Chen, Suhas Diggavi, Shlomo ShamaiAbstract:We investigate whether uncoded schemes are optimal for Gaussian sources on multiuser Gaussian channels. Particularly, we consider two problems: the first is to send correlated Gaussian sources on a Gaussian broadcast channel where each receiver is interested in reconstructing only one source component (or one specific linear function of the sources) under the mean Squared Error Distortion measure; the second is to send correlated Gaussian sources on a Gaussian multiple-access channel, where each transmitter observes a noisy combination of the source, and the receiver wishes to reconstruct the individual source components (or individual linear functions) under the mean Squared Error Distortion measure. It is shown that when the channel parameters match certain general conditions, the induced Distortion tuples are on the boundary of the achievable Distortion region, and thus optimal. Instead of following the conventional approach of attempting to characterize the achievable Distortion region, we ask the question whether and how a match can be effectively determined. This decision problem formulation helps to circumvent the difficult optimization problem often embedded in region characterization problems, and it also leads us to focus on the critical conditions in the outer bounds that make the inequalities become equalities, which effectively decouples the overall problem into several simpler sub-problems.
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Approximating the Gaussian Multiple Description Rate Region Under Symmetric Distortion Constraints
IEEE Transactions on Information Theory, 2009Co-Authors: Chao Tian, Soheil Mohajer, Suhas DiggaviAbstract:We consider multiple description coding for the Gaussian source with K descriptions under the symmetric mean Squared Error Distortion constraints, and provide an approximate characterization of the rate region. We show that the rate region can be sandwiched between two polytopes, between which the gap can be upper bounded by constants dependent on the number of descriptions, but independent of the exact Distortion constraints. Underlying this result is an exact characterization of the lossless multi-level diversity source coding problem: a lossless counterpart of the MD problem. This connection provides a polytopic template for the inner and outer bounds to the rate region. In order to establish the outer bound, we generalize Ozarow's technique to introduce a strategic expansion of the original probability space by more than one random variables. For the symmetric rate case with any number of descriptions, we show that the gap between the upper bound and the lower bound for the individual description rate is no larger than 0.92 bit. The results developed in this work also suggest the "separation" approach of combining successive refinement quantization and lossless multi-level diversity coding is a competitive one, since it is only a constant away from the optimum. The results are further extended to general sources under the mean Squared Error Distortion measure, where a similar but looser bound on the gap holds.
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Multiple Description Coding for Stationary Gaussian Sources
IEEE Transactions on Information Theory, 2009Co-Authors: J. Chen, Chao Tian, Suhas DiggaviAbstract:We consider the problem of multiple description coding for stationary Gaussian sources under the Squared Error Distortion measure. The rate region is characterized for the 2-description case. It is shown that each supporting line of the rate region is achievable with a transform lattice quantization scheme. We show the optimal coding scheme has a natural spectral domain coding interpretation, which yields a reverse water-filling solution with a frequency-dependent water level instead of the flat water level as in the conventional single description case.
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successive refinement via broadcast optimizing expected Distortion of a gaussian source over a gaussian fading channel
Information Theory Workshop, 2008Co-Authors: Chao Tian, Shlomo Shamai, A Steiner, Suhas DiggaviAbstract:We consider the problem of transmitting a Gaussian source on a slowly fading Gaussian channel, subject to the mean-Squared Error Distortion measure. The channel state information is known only at the receiver but not at the transmitter. The source is assumed to be encoded in a successive refinement (SR) manner, and then transmitted over the channel using the broadcast strategy. In order to minimize the expected Distortion at the receiver, optimal power allocation is essential. We propose an efficient algorithm to compute the optimal solution in linear time , when the total number of possible discrete fading states. Moreover, we provide a derivation of the optimal power allocation when the fading state is a continuum, using the classical variational method. The proposed algorithm as well as the continuous solution is based on an alternative representation of the capacity region of the Gaussian broadcast channel.
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DCC - Multiple Description Coding for Stationary and Ergodic Sources
2007 Data Compression Conference (DCC'07), 2007Co-Authors: J. Chen, Chao Tian, Suhas DiggaviAbstract:We consider the problem of multiple description (MD) coding for stationary sources with the Squared Error Distortion measure. The MD rate region is derived for the stationary and ergodic Gaussian sources, and is shown to be achievable with a practical transform lattice quantization scheme. Moreover, the proposed scheme is asymptotically optimal at high resolution for all stationary sources with finite differential entropy rate
Gaurav S Sukhatme - One of the best experts on this subject based on the ideXlab platform.
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Squared Error Distortion metrics for motion planning in robotic sensor networks
Global Communications Conference, 2013Co-Authors: Geoffrey A Hollinger, Chiranjib Choudhuri, Urbashi Mitra, Gaurav S SukhatmeAbstract:We examine the problem of planning the trajectory of a robotic vehicle to gather data from a deployment of stationary sensors monitoring a set of dynamic source signals. The robotic vehicle and the sensors are equipped with wireless modems (e.g., radio in terrestrial environments or acoustic in underwater environments), which provide noisy communication across limited distances. In such scenarios, the robotic vehicle can improve its efficiency by planning an informed data gathering trajectory. We propose a novel performance metric for data gathering in robotic sensor networks based on the concept of Squared Error Distortion. We analyze the formal properties of the Distortion function, and we propose a sampling-based motion planning algorithm for optimizing data gathering tours for minimal Distortion. The proposed algorithms are compared in simulation, and the results show that Distortion metrics provide substantial improvements in data gathering efficiency.
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GLOBECOM Workshops - Squared Error Distortion metrics for motion planning in robotic sensor networks
2013 IEEE Globecom Workshops (GC Wkshps), 2013Co-Authors: Geoffrey A Hollinger, Chiranjib Choudhuri, Urbashi Mitra, Gaurav S SukhatmeAbstract:We examine the problem of planning the trajectory of a robotic vehicle to gather data from a deployment of stationary sensors monitoring a set of dynamic source signals. The robotic vehicle and the sensors are equipped with wireless modems (e.g., radio in terrestrial environments or acoustic in underwater environments), which provide noisy communication across limited distances. In such scenarios, the robotic vehicle can improve its efficiency by planning an informed data gathering trajectory. We propose a novel performance metric for data gathering in robotic sensor networks based on the concept of Squared Error Distortion. We analyze the formal properties of the Distortion function, and we propose a sampling-based motion planning algorithm for optimizing data gathering tours for minimal Distortion. The proposed algorithms are compared in simulation, and the results show that Distortion metrics provide substantial improvements in data gathering efficiency.
Geoffrey A Hollinger - One of the best experts on this subject based on the ideXlab platform.
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Squared Error Distortion metrics for motion planning in robotic sensor networks
Global Communications Conference, 2013Co-Authors: Geoffrey A Hollinger, Chiranjib Choudhuri, Urbashi Mitra, Gaurav S SukhatmeAbstract:We examine the problem of planning the trajectory of a robotic vehicle to gather data from a deployment of stationary sensors monitoring a set of dynamic source signals. The robotic vehicle and the sensors are equipped with wireless modems (e.g., radio in terrestrial environments or acoustic in underwater environments), which provide noisy communication across limited distances. In such scenarios, the robotic vehicle can improve its efficiency by planning an informed data gathering trajectory. We propose a novel performance metric for data gathering in robotic sensor networks based on the concept of Squared Error Distortion. We analyze the formal properties of the Distortion function, and we propose a sampling-based motion planning algorithm for optimizing data gathering tours for minimal Distortion. The proposed algorithms are compared in simulation, and the results show that Distortion metrics provide substantial improvements in data gathering efficiency.
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GLOBECOM Workshops - Squared Error Distortion metrics for motion planning in robotic sensor networks
2013 IEEE Globecom Workshops (GC Wkshps), 2013Co-Authors: Geoffrey A Hollinger, Chiranjib Choudhuri, Urbashi Mitra, Gaurav S SukhatmeAbstract:We examine the problem of planning the trajectory of a robotic vehicle to gather data from a deployment of stationary sensors monitoring a set of dynamic source signals. The robotic vehicle and the sensors are equipped with wireless modems (e.g., radio in terrestrial environments or acoustic in underwater environments), which provide noisy communication across limited distances. In such scenarios, the robotic vehicle can improve its efficiency by planning an informed data gathering trajectory. We propose a novel performance metric for data gathering in robotic sensor networks based on the concept of Squared Error Distortion. We analyze the formal properties of the Distortion function, and we propose a sampling-based motion planning algorithm for optimizing data gathering tours for minimal Distortion. The proposed algorithms are compared in simulation, and the results show that Distortion metrics provide substantial improvements in data gathering efficiency.
