The Experts below are selected from a list of 63 Experts worldwide ranked by ideXlab platform
Yair Weiss - One of the best experts on this subject based on the ideXlab platform.
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Discrete-Input two-dimensional Gaussian channels with memory: Estimation and information rates via graphical models and statistical mechanics
IEEE Transactions on Information Theory, 2008Co-Authors: Ori SHENTAL, Ido Kanter, Noam Shental, Anthony J. Weiss, Shlomo Shamai, Yair WeissAbstract:Discrete-Input two-dimensional (2D) Gaussian channels with memory represent an important class of systems, which appears extensively in communications and storage. In spite of their widespread use, the workings of 2D channels are still very much unknown. In this work, we try to explore their properties from the perspective of estimation theory and information theory. At the heart of our approach is a mapping of a 2D channel to an undirected graphical model, and inferring its a posteriori probabilities (APPs) using generalized belief propagation (GBP). The derived probabilities are shown to be practically accurate, thus enabling optimal maximum a posteriori (MAP) estimation of the transmitted symbols. Also, the Shannon-theoretic information rates are deduced either via the vector-wise Shannon-McMillan-Breiman (SMB) theorem, or via the recently derived symbol-wise Guo-Shamai-Verdu (GSV) theorem. Our approach is also described from the perspective of statistical mechanics, as the graphical model and inference algorithm have their analogues in physics. Our experimental study, based on common channel settings taken from cellular networks and magnetic recording devices, demonstrates that under nontrivial memory conditions, the performance of this fully tractable GBP estimator is almost identical to the performance of the optimal MAP estimator. It also enables a practically accurate simulation-based estimate of the information rate. Rationalization of this excellent performance of GBP in the 2-D Gaussian channel setting is addressed.
Meik Dorpinghaus - One of the best experts on this subject based on the ideXlab platform.
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threshold optimization for capacity achieving Discrete Input one bit output quantization
International Symposium on Information Theory, 2013Co-Authors: Rudolf Matha, Meik DorpinghausAbstract:In this paper, we consider one-bit output quantization of a Discrete signal with m real signaling points subject to arbitrary additive noise. First, the capacity-achieving distribution is determined for the corresponding channel. For any fixed quantization threshold q it concentrates on the two most distant signaling points, hence leading to an interpretation as binary asymmetric channel. The direct proof of this result allows for an explicit form of the capacity as a function of threshold q. We characterize stationary points as candidates for optimal thresholds by a condition on the differential quotient of the derivative of the binary entropy function. In contrast to intuition, symmetry of the noise distribution does not ensure a unique optimum antipodal threshold.
A Chockalingam - One of the best experts on this subject based on the ideXlab platform.
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precoding by pairing subchannels to increase mimo capacity with Discrete Input alphabets
IEEE Transactions on Information Theory, 2011Co-Authors: Saif Kha Mohammed, Emanuele Viterbo, Yi Hong, A ChockalingamAbstract:We consider Gaussian multiple-Input multiple-output (MIMO) channels with Discrete Input alphabets. We propose a non diagonal precoder based on the X-Codes in to increase the mutual information. The MIMO channel is transformed into a set of parallel subchannels using singular value decomposition (SVD) and X-Codes are then used to pair the subchannels. X-Codes are fully characterized by the pairings and a 2 × 2 real rotation matrix for each pair (parameterized with a single angle). This precoding structure enables us to express the total mutual information as a sum of the mutual information of all the pairs. The problem of finding the optimal precoder with the above structure, which maximizes the total mutual information, is solved by: i) optimizing the rotation angle and the power allocation within each pair and ii) finding the optimal pairing and power allocation among the pairs. It is shown that the mutual information achieved with the proposed pairing scheme is very close to that achieved with the optimal pre coder by Cruz et al., and is significantly better than Mercury/waterfllling strategy by Lozano et al. Our approach greatly simplifies both the precoder optimization and the detection complexity, making it suitable for practical applications.
Daniel E Quevedo - One of the best experts on this subject based on the ideXlab platform.
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stability analysis of quadratic mpc with a Discrete Input alphabet
IEEE Transactions on Automatic Control, 2013Co-Authors: Ricardo P Aguilera, Daniel E QuevedoAbstract:We study stability of Model Predictive Control (MPC) with a quadratic cost function for LTI systems with a Discrete Input alphabet. Since this kind of systems may present a steady-state error, the focus is on practical stability, i.e., ultimate boundedness of solutions. To derive sufficient conditions for practical stability and characterize the ultimately invariant set, we analyze the one-step horizon solution and adapt tools used for convex MPC formulations.
Peter Sollich - One of the best experts on this subject based on the ideXlab platform.
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exact learning curves for gaussian process regression on large random graphs
Neural Information Processing Systems, 2010Co-Authors: Matthew Urry, Peter SollichAbstract:We study learning curves for Gaussian process regression which characterise performance in terms of the Bayes error averaged over datasets of a given size. Whilst learning curves are in general very difficult to calculate we show that for Discrete Input domains, where similarity between Input points is characterised in terms of a graph, accurate predictions can be obtained. These should in fact become exact for large graphs drawn from a broad range of random graph ensembles with arbitrary degree distributions where each Input (node) is connected only to a finite number of others. Our approach is based on translating the appropriate belief propagation equations to the graph ensemble. We demonstrate the accuracy of the predictions for Poisson (Erdos-Renyi) and regular random graphs, and discuss when and why previous approximations of the learning curve fail.
