The Experts below are selected from a list of 159 Experts worldwide ranked by ideXlab platform
Andreas Winter - One of the best experts on this subject based on the ideXlab platform.
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Distributed Compression of correlated classical quantum sources or the price of ignorance
IEEE Transactions on Information Theory, 2020Co-Authors: Zahra Baghali Khanian, Andreas WinterAbstract:We resume the investigation of the problem of independent local Compression of correlated quantum sources, the classical case of which is covered by the celebrated Slepian-Wolf theorem. We focus specifically on classical-quantum (cq) sources, for which one edge of the rate region, corresponding to the Compression of the classical part, using the quantum part as side information at the decoder, was previously determined by Devetak and Winter [Phys. Rev. A 68, 042301 (2003)]. Whereas the Devetak-Winter protocol attains a rate-sum equal to the von Neumann entropy of the joint source, here we show that the full rate region is much more complex, due to the partially quantum nature of the source. In particular, in the opposite case of compressing the quantum part of the source, using the classical part as side information at the decoder, typically the rate sum is strictly larger than the von Neumann entropy of the total source. We determine the full rate region in the generic case, showing that, apart from the Devetak-Winter point, all other points in the achievable region have a rate sum strictly larger than the joint entropy. We can interpret the difference as the price paid for the quantum encoder being ignorant of the classical side information. In the general case, we give an achievable rate region, via protocols that are built on the decoupling principle, and the protocols of quantum state merging and quantum state redistribution. Our achievable region is matched almost by a single-letter converse, which however still involves asymptotic errors and an unbounded auxiliary system.
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Distributed Compression of correlated classical quantum sources or the price of ignorance
arXiv: Quantum Physics, 2018Co-Authors: Zahra Baghali Khanian, Andreas WinterAbstract:We resume the investigation of the problem of independent local Compression of correlated quantum sources, the classical case of which is covered by the celebrated Slepian-Wolf theorem. We focus specifically on classical-quantum (cq) sources, for which one edge of the rate region, corresponding to the Compression of the classical part, using the quantum part as side information at the decoder, was previously determined by Devetak and Winter [Phys. Rev. A 68, 042301 (2003)]. Whereas the Devetak-Winter protocol attains a rate-sum equal to the von Neumann entropy of the joint source, here we show that the full rate region is much more complex, due to the partially quantum nature of the source. In particular, in the opposite case of compressing the quantum part of the source, using the classical part as side information at the decoder, typically the rate sum is strictly larger than the von Neumann entropy of the total source. We determine the full rate region in the generic case, showing that, apart from the Devetak-Winter point, all other points in the achievable region have a rate sum strictly larger than the joint entropy. We can interpret the difference as the price paid for the quantum encoder being ignorant of the classical side information. In the general case, we give an achievable rate region, via protocols that are built on the decoupling principle, and the principles of quantum state merging and quantum state redistribution. Our achievable region is matched almost by a single-letter converse, which however still involves asymptotic errors and an unbounded auxiliary system.
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the mother of all protocols restructuring quantum information s family tree
Proceedings of The Royal Society A: Mathematical Physical and Engineering Sciences, 2009Co-Authors: Anura Abeyesinghe, Patrick Hayden, Igor Devetak, Andreas WinterAbstract:We give a simple, direct proof of the ‘mother’ protocol of quantum information theory. In this new formulation, it is easy to see that the mother, or rather her generalization to the fully quantum Slepian–Wolf protocol, simultaneously accomplishes two goals: quantum communication-assisted entanglement distillation and state transfer from the sender to the receiver. As a result, in addition to her other ‘children’, the mother protocol generates the state-merging primitive of Horodecki, Oppenheim and Winter, a fully quantum reverse Shannon theorem, and a new class of Distributed Compression protocols for correlated quantum sources which are optimal for sources described by separable density operators. Moreover, the mother protocol described here is easily transformed into the so-called ‘father’ protocol whose children provide the quantum capacity and the entanglement-assisted capacity of a quantum channel, demonstrating that the division of single-sender/single-receiver protocols into two families was unnecessary: all protocols in the family are children of the mother.
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on the Distributed Compression of quantum information
IEEE Transactions on Information Theory, 2006Co-Authors: Charlene Ahn, Andrew C Doherty, Patrick Hayden, Andreas WinterAbstract:The problem of Distributed Compression for correlated quantum sources is considered. The classical version of this problem was solved by Slepian and Wolf, who showed that Distributed Compression could take full advantage of redundancy in the local sources created by the presence of correlations. Here it is shown that, in general, this is not the case for quantum sources, by proving a lower bound on the rate sum for irreducible sources of product states which is stronger than the one given by a naive application of Slepian-Wolf. Nonetheless, strategies taking advantage of correlation do exist for some special classes of quantum sources. For example, Devetak and Winter demonstrated the existence of such a strategy when one of the sources is classical. Optimal nontrivial strategies for a different extreme, sources of Bell states, are presented here. In addition, it is explained how Distributed Compression is connected to other problems in quantum information theory, including information-disturbance questions, entanglement distillation and quantum error correction
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partial quantum information
Nature, 2005Co-Authors: Michal Horodecki, Jonathan Oppenheim, Andreas WinterAbstract:Information—be it classical1 or quantum2—is measured by the amount of communication needed to convey it. In the classical case, if the receiver has some prior information about the messages being conveyed, less communication is needed3. Here we explore the concept of prior quantum information: given an unknown quantum state Distributed over two systems, we determine how much quantum communication is needed to transfer the full state to one system. This communication measures the partial information one system needs, conditioned on its prior information. We find that it is given by the conditional entropy—a quantity that was known previously, but lacked an operational meaning. In the classical case, partial information must always be positive, but we find that in the quantum world this physical quantity can be negative. If the partial information is positive, its sender needs to communicate this number of quantum bits to the receiver; if it is negative, then sender and receiver instead gain the corresponding potential for future quantum communication. We introduce a protocol that we term ‘quantum state merging’ which optimally transfers partial information. We show how it enables a systematic understanding of quantum network theory, and discuss several important applications including Distributed Compression, noiseless coding with side information, multiple access channels and assisted entanglement distillation.
Kannan Ramchandran - One of the best experts on this subject based on the ideXlab platform.
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prism a video coding paradigm with motion estimation at the decoder
IEEE Transactions on Image Processing, 2007Co-Authors: R Puri, A Majumdar, Kannan RamchandranAbstract:We describe PRISM, a video coding paradigm based on the principles of lossy Distributed Compression (also called source coding with side information or Wyner-Ziv coding) from multiuser information theory. PRISM represents a major departure from conventional video coding architectures (e.g., the MPEGx, H.26x families) that are based on motion-compensated predictive coding, with the goal of addressing some of their architectural limitations. PRISM allows for two key architectural enhancements: (1) inbuilt robustness to "drift" between encoder and decoder and (2) the feasibility of a flexible distribution of computational complexity between encoder and decoder. Specifically, PRISM enables transfer of the computationally expensive video encoder motion-search module to the video decoder. Based on this capability, we consider an instance of PRISM corresponding to a near reversal in codec complexities with respect to today's codecs (leading to a novel light encoder and heavy decoder paradigm), in this paper. We present encouraging preliminary results on real-world video sequences, particularly in the realm of transmission losses, where PRISM exhibits the characteristic of rapid recovery, in contrast to contemporary codecs. This renders PRISM as an attractive candidate for wireless video applications.
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dither based secure image hashing using Distributed coding
International Conference on Image Processing, 2003Co-Authors: Mark Johnson, Kannan RamchandranAbstract:We propose an image hashing algorithm that is based on Distributed Compression principles. The algorithm assumes the availability of a robust feature vector extracted from the image. Then a suitable dither sequence is added to this feature vector, and the resulting dithered feature vector is compressed using Distributed Compression principles. We prove that the dither sequence can be used to guarantee information-theoretic security. Applications of our proposed secure image hashing scheme include video watermarking, image authentication, and image database management.
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towards a theory for video coding using Distributed Compression principles
International Conference on Image Processing, 2003Co-Authors: Prakash Ishwar, Vinod M Prabhakaran, Kannan RamchandranAbstract:This paper presents an information-theoretic study of video codecs that are based on the principle of source coding with side information at the decoder. In contrast to the classical Wyner-Ziv side-information source coding problem (1976), in this work we address the situation where the source and side-information are connected through a state of nature that is unknown to both the encoder and the decoder. We dub this framework as source encoding with side-information under ambiguous state of nature (SEASON). Our objective is to compare the achievable rate-distortion (R/D) performance of conventional video codecs designed under the motion-compensated predictive coding (MCPC) framework and video codecs designed under the SEASON framework. Our analysis shows that under appropriate motion models and for Gaussian displaced frame difference (DFD) statistics, the R/D performance of a classical MCPC-based video codec is matched by that of our proposed SEASON-based video codec, with the hitter being characterized by the novel concept of moving the motion compensation task from the encoder to the decoder.
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Distributed Compression in a dense microsensor network
IEEE Signal Processing Magazine, 2002Co-Authors: S.s. Pradhan, Julius Kusuma, Kannan RamchandranAbstract:Distributed nature of the sensor network architecture introduces unique challenges and opportunities for collaborative networked signal processing techniques that can potentially lead to significant performance gains. Many evolving low-power sensor network scenarios need to have high spatial density to enable reliable operation in the face of component node failures as well as to facilitate high spatial localization of events of interest. This induces a high level of network data redundancy, where spatially proximal sensor readings are highly correlated. We propose a new way of removing this redundancy in a completely Distributed manner, i.e., without the sensors needing to talk, to one another. Our constructive framework for this problem is dubbed DISCUS (Distributed source coding using syndromes) and is inspired by fundamental concepts from information theory. We review the main ideas, provide illustrations, and give the intuition behind the theory that enables this framework.We present a new domain of collaborative information communication and processing through the framework on Distributed source coding. This framework enables highly effective and efficient Compression across a sensor network without the need to establish inter-node communication, using well-studied and fast error-correcting coding algorithms
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Distributed Compression for sensor networks
International Conference on Image Processing, 2001Co-Authors: Julius Kusuma, L Doherty, Kannan RamchandranAbstract:We consider the problem of efficiently transmitting sets of spatially correlated observations in a Distributed sensor network without requiring inter-node communication to exploit the correlation. Specifically, we provide a construction for quantizer design given a training set, and a Distributed Compression scheme to efficiently relay the quantized observations to a central decoder.
Shlomo Shamai - One of the best experts on this subject based on the ideXlab platform.
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robust and efficient Distributed Compression for cloud radio access networks
IEEE Transactions on Vehicular Technology, 2013Co-Authors: Seokhwan Park, Osvaldo Simeone, Onur Sahin, Shlomo ShamaiAbstract:This paper studies Distributed Compression for the uplink of a cloud radio access network where multiple multiantenna base stations (BSs) are connected to a central unit, which is also referred to as a cloud decoder, via capacity-constrained backhaul links. Since the signals received at different BSs are correlated, Distributed source coding strategies are potentially beneficial. However, they require each BS to have information about the joint statistics of the received signals across the BSs, and they are generally sensitive to uncertainties regarding such information. Motivated by this observation, a robust Compression method is proposed to cope with uncertainties on the correlation of the received signals. The problem is formulated using a deterministic worst case approach, and an algorithm is proposed that achieves a stationary point for the problem. Then, BS selection is addressed with the aim of reducing the number of active BSs, thus enhancing the energy efficiency of the network. An optimization problem is formulated in which Compression and BS selection are performed jointly by introducing a sparsity-inducing term into the objective function. An iterative algorithm is proposed that is shown to converge to a locally optimal point. From numerical results, it is observed that the proposed robust Compression scheme compensates for a large fraction of the performance loss induced by the imperfect statistical information. Moreover, the proposed BS selection algorithm is seen to perform close to the more complex exhaustive search solution.
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joint base station selection and Distributed Compression for cloud radio access networks
Global Communications Conference, 2012Co-Authors: Seokhwan Park, Osvaldo Simeone, Onur Sahin, Shlomo ShamaiAbstract:This work studies joint base station (BS) selection and Distributed Compression for the uplink of a cloud radio access network. Multiple multi-antenna BSs are connected to a central unit, also referred to as cloud decoder, via capacity-constrained backhaul links. Since the signals received at different BSs are correlated, Distributed source coding strategies for communication to the cloud decoder are potentially beneficial. Moreover, reducing the number of active BSs can improve the network energy efficiency, since BS energy consumption provides a major contribution to the overall energy expenditure for the network. An optimization problem is formulated in which Compression and BS selection are performed jointly by introducing a sparsity-inducing term into the objective function. An iterative algorithm is proposed. From numerical results, it is observed that the proposed joint BS selection and Compression algorithm performs close to the more complex exhaustive search solution.
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robust Distributed Compression for cloud radio access networks
Information Theory Workshop, 2012Co-Authors: Seokhwan Park, Osvaldo Simeone, Onur Sahin, Shlomo ShamaiAbstract:This work studies Distributed Compression for the uplink of a cloud radio access network, where multiple multi-antenna base stations (BSs) communicate with a central unit, also referred to as cloud decoder, via capacity-constrained back-haul links. Distributed source coding strategies are potentially beneficial since the signals received at different BSs are correlated. However, they require each BS to have information about the joint statistics of the received signals across the BSs, and are generally sensitive to uncertainties regarding such information. Motivated by this observation, a robust Compression method is proposed to cope with uncertainties on the correlation of the received signals. The problem is formulated using a deterministic worst-case approach, and an algorithm is proposed that achieves a stationary point for the problem. From numerical results, it is observed that the proposed robust Compression scheme compensates for a large fraction of the performance loss induced by the imperfect statistical information.
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robust and efficient Distributed Compression for cloud radio access networks
arXiv: Information Theory, 2012Co-Authors: Seokhwan Park, Osvaldo Simeone, Onur Sahin, Shlomo ShamaiAbstract:This work studies Distributed Compression for the uplink of a cloud radio access network where multiple multi-antenna base stations (BSs) are connected to a central unit, also referred to as cloud decoder, via capacity-constrained backhaul links. Since the signals received at different BSs are correlated, Distributed source coding strategies are potentially beneficial, and can be implemented via sequential source coding with side information. For the problem of Compression with side information, available Compression strategies based on the criteria of maximizing the achievable rate or minimizing the mean square error are reviewed first. It is observed that, in either case, each BS requires information about a specific covariance matrix in order to realize the advantage of Distributed source coding. Since this covariance matrix depends on the channel realizations corresponding to other BSs, a robust Compression method is proposed for a practical scenario in which the information about the covariance available at each BS is imperfect. The problem is formulated using a deterministic worst-case approach, and an algorithm is proposed that achieves a stationary point for the problem. Then, BS selection is addressed with the aim of reducing the number of active BSs, thus enhancing the energy efficiency of the network. An optimization problem is formulated in which Compression and BS selection are performed jointly by introducing a sparsity-inducing term into the objective function. An iterative algorithm is proposed that is shown to converge to a locally optimal point. From numerical results, it is observed that the proposed robust Compression scheme compensates for a large fraction of the performance loss induced by the imperfect statistical information. Moreover, the proposed BS selection algorithm is seen to perform close to the more complex exhaustive search solution.
Seokhwan Park - One of the best experts on this subject based on the ideXlab platform.
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robust and efficient Distributed Compression for cloud radio access networks
IEEE Transactions on Vehicular Technology, 2013Co-Authors: Seokhwan Park, Osvaldo Simeone, Onur Sahin, Shlomo ShamaiAbstract:This paper studies Distributed Compression for the uplink of a cloud radio access network where multiple multiantenna base stations (BSs) are connected to a central unit, which is also referred to as a cloud decoder, via capacity-constrained backhaul links. Since the signals received at different BSs are correlated, Distributed source coding strategies are potentially beneficial. However, they require each BS to have information about the joint statistics of the received signals across the BSs, and they are generally sensitive to uncertainties regarding such information. Motivated by this observation, a robust Compression method is proposed to cope with uncertainties on the correlation of the received signals. The problem is formulated using a deterministic worst case approach, and an algorithm is proposed that achieves a stationary point for the problem. Then, BS selection is addressed with the aim of reducing the number of active BSs, thus enhancing the energy efficiency of the network. An optimization problem is formulated in which Compression and BS selection are performed jointly by introducing a sparsity-inducing term into the objective function. An iterative algorithm is proposed that is shown to converge to a locally optimal point. From numerical results, it is observed that the proposed robust Compression scheme compensates for a large fraction of the performance loss induced by the imperfect statistical information. Moreover, the proposed BS selection algorithm is seen to perform close to the more complex exhaustive search solution.
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joint base station selection and Distributed Compression for cloud radio access networks
Global Communications Conference, 2012Co-Authors: Seokhwan Park, Osvaldo Simeone, Onur Sahin, Shlomo ShamaiAbstract:This work studies joint base station (BS) selection and Distributed Compression for the uplink of a cloud radio access network. Multiple multi-antenna BSs are connected to a central unit, also referred to as cloud decoder, via capacity-constrained backhaul links. Since the signals received at different BSs are correlated, Distributed source coding strategies for communication to the cloud decoder are potentially beneficial. Moreover, reducing the number of active BSs can improve the network energy efficiency, since BS energy consumption provides a major contribution to the overall energy expenditure for the network. An optimization problem is formulated in which Compression and BS selection are performed jointly by introducing a sparsity-inducing term into the objective function. An iterative algorithm is proposed. From numerical results, it is observed that the proposed joint BS selection and Compression algorithm performs close to the more complex exhaustive search solution.
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robust Distributed Compression for cloud radio access networks
Information Theory Workshop, 2012Co-Authors: Seokhwan Park, Osvaldo Simeone, Onur Sahin, Shlomo ShamaiAbstract:This work studies Distributed Compression for the uplink of a cloud radio access network, where multiple multi-antenna base stations (BSs) communicate with a central unit, also referred to as cloud decoder, via capacity-constrained back-haul links. Distributed source coding strategies are potentially beneficial since the signals received at different BSs are correlated. However, they require each BS to have information about the joint statistics of the received signals across the BSs, and are generally sensitive to uncertainties regarding such information. Motivated by this observation, a robust Compression method is proposed to cope with uncertainties on the correlation of the received signals. The problem is formulated using a deterministic worst-case approach, and an algorithm is proposed that achieves a stationary point for the problem. From numerical results, it is observed that the proposed robust Compression scheme compensates for a large fraction of the performance loss induced by the imperfect statistical information.
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robust and efficient Distributed Compression for cloud radio access networks
arXiv: Information Theory, 2012Co-Authors: Seokhwan Park, Osvaldo Simeone, Onur Sahin, Shlomo ShamaiAbstract:This work studies Distributed Compression for the uplink of a cloud radio access network where multiple multi-antenna base stations (BSs) are connected to a central unit, also referred to as cloud decoder, via capacity-constrained backhaul links. Since the signals received at different BSs are correlated, Distributed source coding strategies are potentially beneficial, and can be implemented via sequential source coding with side information. For the problem of Compression with side information, available Compression strategies based on the criteria of maximizing the achievable rate or minimizing the mean square error are reviewed first. It is observed that, in either case, each BS requires information about a specific covariance matrix in order to realize the advantage of Distributed source coding. Since this covariance matrix depends on the channel realizations corresponding to other BSs, a robust Compression method is proposed for a practical scenario in which the information about the covariance available at each BS is imperfect. The problem is formulated using a deterministic worst-case approach, and an algorithm is proposed that achieves a stationary point for the problem. Then, BS selection is addressed with the aim of reducing the number of active BSs, thus enhancing the energy efficiency of the network. An optimization problem is formulated in which Compression and BS selection are performed jointly by introducing a sparsity-inducing term into the objective function. An iterative algorithm is proposed that is shown to converge to a locally optimal point. From numerical results, it is observed that the proposed robust Compression scheme compensates for a large fraction of the performance loss induced by the imperfect statistical information. Moreover, the proposed BS selection algorithm is seen to perform close to the more complex exhaustive search solution.
Pier Luigi Dragotti - One of the best experts on this subject based on the ideXlab platform.
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geometry driven Distributed Compression of the plenoptic function performance bounds and constructive algorithms
IEEE Transactions on Image Processing, 2009Co-Authors: N Gehrig, Pier Luigi DragottiAbstract:In this paper, we study the sampling and the Distributed Compression of the data acquired by a camera sensor network. The effective design of these sampling and Compression schemes requires, however, the understanding of the structure of the acquired data. To this end, we show that the a priori knowledge of the configuration of the camera sensor network can lead to an effective estimation of such structure and to the design of effective Distributed Compression algorithms. For idealized scenarios, we derive the fundamental performance bounds of a camera sensor network and clarify the connection between sampling and Distributed Compression. We then present a Distributed Compression algorithm that takes advantage of the structure of the data and that outperforms independent Compression algorithms on real multiview images.
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Distributed source coding theory algorithms and applications
2009Co-Authors: Pier Luigi Dragotti, Michael GastparAbstract:The advent of wireless sensor technology and ad-hoc networks has made DSC a major field of interest. Edited and written by the leading players in the field, this book presents the latest theory, algorithms and applications, making it the definitive reference on DSC for systems designers and implementers, researchers, and graduate students.This book gives a clear understanding of the performance limits of Distributed source coders for specific classes of sources and presents the design and application of practical algorithms for realistic scenarios. Material covered includes the use of standard channel codes, such as LDPC and Turbo codes, to DSC, and discussion of the suitability of compressed sensing for Distributed Compression of sparse signals. Extensive applications are presented and include Distributed video coding, microphone arrays and securing biometric data.This book is a great resource covering the breadth and depth of Distributed source coding that's appropriate for everyone from theoreticians to practitioners. - Richard Baraniuk, Rice UniversityPier Luigi Dragotti is currently a Senior Lecturer in the Electrical and Electronic Engineering Department at Imperial College, London. He has worked as a researcher at Bell Labs and EPFL and is a member of the IEEE Image and MultiDimensional Signal Processing (IMDSP) Technical Committee.Michael Gastpar is currently an Assistant Professor at the University of California, Berkeley. His research interests are in network information theory and related coding and signal processing techniques, with applications to sensor networks and neuroscience. He won the 2002 EPFL Best Thesis Award, an NSF CAREER award in 2004, and an Okawa Foundation Research Grant in 2008. *Clear explanation of the principles of Distributed source coding (DSC), a technology that has applications in sensor networks, ad-hoc networks, and Distributed wireless video systems for surveillance*Edited and written by the leading players in the field, providing a complete and authoritative reference*Contains all the latest theory, practical algorithms for DSC design and the most recently developed applications
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Distributed Compression of the plenoptic function
International Conference on Image Processing, 2004Co-Authors: N Gehrig, Pier Luigi DragottiAbstract:In this paper, we consider the problem of Distributed Compression in camera sensor networks. Due to the spatial proximity of the different cameras, acquired images can be highly dependent. The correlation in the visual information retrieved is related to the structure of the plenoptic function and can be estimated using geometrical information such as the position of the cameras and some bounds on the location of the objects. We propose a Distributed Compression scheme that takes advantage of this geometrical information in order to reduce the overall transmission rate from the sensors to a common central receiver. This new approach allows for a flexible repartition of the transmission bit-rates amongst the encoders and is optimal in many cases. Moreover, we show that our coding scheme can be made resilient to a fixed number of occlusions and that perfect reconstruction and interpolation are possible at the receiver.