The Experts below are selected from a list of 7809 Experts worldwide ranked by ideXlab platform
Junichi Takeuchi - One of the best experts on this subject based on the ideXlab platform.
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An Improved Analysis of Least Squares Superposition Codes with Bernoulli Dictionary
arXiv: Information Theory, 2018Co-Authors: Yoshinari Takeishi, Junichi TakeuchiAbstract:For the additive white Gaussian noise channel with Average Power Constraint, sparse superposition codes, proposed by Barron and Joseph in 2010, achieve the capacity. While the codewords of the original sparse superposition codes are made with a dictionary matrix drawn from a Gaussian distribution, we consider the case that it is drawn from a Bernoulli distribution. We show an improved upper bound on its block error probability with least squares decoding, which is fairly simplified and tighter bound than our previous result in 2014.
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an improved upper bound on block error probability of least squares superposition codes with unbiased bernoulli dictionary
International Symposium on Information Theory, 2016Co-Authors: Yoshinari Takeishi, Junichi TakeuchiAbstract:For the additive white Gaussian noise channel with Average Power Constraint, it is shown that sparse superposition codes, proposed by Barron and Joseph in 2010, achieve the capacity. We study the upper bounds on its block error probability with least squares decoding when a dictionary with which we make codewords is drawn from an unbiased Bernoulli distribution. We improve the upper bounds shown by Takeishi et.al. in 2014 with fairly simplified form.
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ISIT - An improved upper bound on block error probability of least squares superposition codes with unbiased Bernoulli dictionary
2016 IEEE International Symposium on Information Theory (ISIT), 2016Co-Authors: Yoshinari Takeishi, Junichi TakeuchiAbstract:For the additive white Gaussian noise channel with Average Power Constraint, it is shown that sparse superposition codes, proposed by Barron and Joseph in 2010, achieve the capacity. We study the upper bounds on its block error probability with least squares decoding when a dictionary with which we make codewords is drawn from an unbiased Bernoulli distribution. We improve the upper bounds shown by Takeishi et.al. in 2014 with fairly simplified form.
Shlomo Shamai - One of the best experts on this subject based on the ideXlab platform.
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Transition points in the capacity-achieving distribution for free-space optical intensity channels
IEEE Information Theory Workshop 2010 (ITW 2010), 2010Co-Authors: Naresh Sharma, Shlomo ShamaiAbstract:The capacity-achieving input distribution for the free-space optical intensity channels with the peak Power Constraint with or without the Average Power Constraint is known to be discrete. We are interested in the structure of the capacity-achieving distribution at the transition points where the number of mass points increases. We give the necessary and sufficient conditions for the transition to occur. We consider two regimes depending on whether the Average Power Constraint is active or not, and both the regimes have the peak Power Constraint. For the regime where the Average Power Constraint is active, we give explicit conditions for the binary to ternary transition. For the regime where the Average Power Constraint is inactive, we show that our previous work [1] on the structure of the capacity-achieving distribution at the transition points for the Smith's case with only the peak-Power Constraint [2] is applicable.
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ICASSP (4) - Linear MIMO precoders for fixed receivers
2004 IEEE International Conference on Acoustics Speech and Signal Processing, 2004Co-Authors: Ami Wiesel, Yonina C Eldar, Shlomo ShamaiAbstract:We consider the problem of designing linear multiple input multiple output (MIMO) precoders for fixed receivers. We first derive a precoder that minimizes the Average Power subject to signal to interference plus noise ratio (SINR) Constraints, and then derive a precoder that maximizes the worst case SINR subject to an Average Power Constraint. We show that both problems can be solved using standard optimization packages. In addition, a more efficient solution based on Karush-Kuhn-Tucker (KKT) optimality conditions is presented which gives more insight into the problem. Our design promises equal SINR and fairness among the multiple outputs. Simulation results in a multiuser system show that the proposed precoders can significantly outperform existing linear precoders.
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the capacity of discrete time memoryless rayleigh fading channels
IEEE Transactions on Information Theory, 2001Co-Authors: Ibrahim Aboufaycal, M D Trott, Shlomo ShamaiAbstract:We consider transmission over a discrete-time Rayleigh fading channel, in which successive symbols face independent fading, and where neither the transmitter nor the receiver has channel state information. Subject to an Average Power Constraint, we study the capacity-achieving distribution of this channel and prove it to be discrete with a finite number of mass points, one of them located at the origin. We numerically compute the capacity and the corresponding optimal distribution as a function of the signal-to-noise ratio (SNR). The behavior of the channel at low SNR is studied and finally a comparison is drawn with the ideal additive white Gaussian noise channel.
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the capacity of discrete time rayleigh fading channels
International Symposium on Information Theory, 1997Co-Authors: Ibrahim Aboufaycal, M D Trott, Shlomo ShamaiAbstract:We consider transmission over a discrete-time Rayleigh fading channel, in which successive symbols face independent fading, and where neither the transmitter nor the receiver has channel state information. Subject to an Average Power Constraint, we study the capacity-achieving distribution of this channel and prove it to be discrete with a finite number of mass points, one of them located at the origin. We develop an algorithm for computing capacity and the corresponding optimal distribution.
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On the capacity of a direct-detection photon channel with intertransition-constrained binary input
IEEE Transactions on Information Theory, 1991Co-Authors: Shlomo ShamaiAbstract:The classical directed detection photon channel is modeled by an output nu /sub t/ (observed signal) describing the photon-arrival Poisson (count) process with intensity (rate) lambda /sub t/+ lambda /sub 0/, where lambda /sub t/ (photons/s) is the channel input (information carrying) intensity and lambda /sub 0/ (photons/s) is the dark current intensity. Upper and lower bounds on the capacity of this channel are presented for two-level (binary) inputs taking on the extreme value lambda /sub t/ in (0,A), where A denotes the peak Power satisfying an Average Power Constraint E( lambda /sub t/) >
H Leib - One of the best experts on this subject based on the ideXlab platform.
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shannon capacity and eigen beamforming for space dispersive multipath mimo channels
Wireless Communications and Networking Conference, 2003Co-Authors: M Kassouf, H LeibAbstract:This paper considers the information transfer capacity of a space dispersive multipath channel with multiple antenna at the transmitter and receiver. The Shannon capacity is evaluated for such multiple-input multiple-output (MIMO) channels with multi-dimensional space-time modulation. Assuming a non-fading environment and a known channel at the transmitter and receiver, we derive the Shannon capacity under a transmit Average Power Constraint. It is shown that the signal structure achieving capacity corresponds to eigen-beamforming. Capacity is shown to increase with the number of signal propagation paths. The effect of the space-time modulation format on the information-theoretic capacity is also pointed out.
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WCNC - Shannon capacity and eigen-beamforming for space dispersive multipath MIMO channels
2003 IEEE Wireless Communications and Networking 2003. WCNC 2003., 1Co-Authors: M Kassouf, H LeibAbstract:This paper considers the information transfer capacity of a space dispersive multipath channel with multiple antenna at the transmitter and receiver. The Shannon capacity is evaluated for such multiple-input multiple-output (MIMO) channels with multi-dimensional space-time modulation. Assuming a non-fading environment and a known channel at the transmitter and receiver, we derive the Shannon capacity under a transmit Average Power Constraint. It is shown that the signal structure achieving capacity corresponds to eigen-beamforming. Capacity is shown to increase with the number of signal propagation paths. The effect of the space-time modulation format on the information-theoretic capacity is also pointed out.
João Ribeiro - One of the best experts on this subject based on the ideXlab platform.
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Improved Upper Bounds and Structural Results on the Capacity of the Discrete-Time Poisson Channel
IEEE Transactions on Information Theory, 2019Co-Authors: Cheraghchi, João RibeiroAbstract:New capacity upper bounds are presented for the discrete-time Poisson channel with no dark current and an Average-Power Constraint. These bounds are a consequence of techniques developed for the seemingly unrelated problem of upper bounding the capacity of binary deletion and repetition channels. Previously, the best known capacity upper bound in the regime where the Average-Power Constraint does not approach zero was due to Martinez (JOSA B, 2007), which is re-derived as a special case of the framework developed in this paper. Furthermore, this framework is carefully instantiated in order to obtain a closed-form bound that improves the result of Martinez everywhere. Finally, capacity-achieving distributions for the discrete-time Poisson channel are studied under an Average-Power Constraint and/or a peak-Power Constraint and arbitrary dark current. In particular, it is shown that the support of the capacity-achieving distribution under an Average-Power Constraint must only be countably infinite. This settles a conjecture of Shamai (IEE Proceedings I, 1990) in the affirmative. Previously, it was only known that the support must be an unbounded set.
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Structural Results and Improved Upper Bounds on the Capacity of the Discrete-Time Poisson Channel
arXiv: Information Theory, 2018Co-Authors: Cheraghchi, João RibeiroAbstract:New capacity upper bounds are presented for the discrete-time Poisson channel with no dark current and an Average-Power Constraint. These bounds are a simple consequence of techniques developed for the seemingly unrelated problem of upper bounding the capacity of binary deletion and repetition channels. Previously, the best known capacity upper bound in the regime where the Average-Power Constraint does not approach zero was due to Martinez (JOSA B, 2007), which is re-derived as a special case of the framework developed in this paper. Furthermore, this framework is carefully instantiated in order to obtain a closed-form bound that noticeably improves the result of Martinez everywhere. Finally, capacity-achieving distributions for the discrete-time Poisson channel are studied under an Average-Power Constraint and/or a peak-Power Constraint and arbitrary dark current. In particular, it is shown that the support of the capacity-achieving distribution under an Average-Power Constraint only must be countably infinite. This settles a conjecture of Shamai (IEE Proceedings I, 1990) in the affirmative. Previously, it was only known that the support must be unbounded.
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Improved Capacity Upper Bounds for the Discrete-Time Poisson Channel
2018Co-Authors: Cheraghchi, João RibeiroAbstract:We present new capacity upper bounds for the discrete-time Poisson channel with no dark current and an Average-Power Constraint. These bounds are a simple consequence of techniques developed by one of the authors for the seemingly unrelated problem of upper bounding the capacity of binary deletion and repetition channels. Previously, the best known capacity upper bound in the regime where the Average-Power Constraint does not approach zero was due to Martinez (JOSA B, 2007), which we re-derive as a special case of our framework. Furthermore, we instantiate our framework to obtain a closed-form bound that noticeably improves the result of Martinez everywhere.
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ISIT - Improved Capacity Upper Bounds for the Discrete-Time Poisson Channel
2018 IEEE International Symposium on Information Theory (ISIT), 2018Co-Authors: Cheraghchi, João RibeiroAbstract:We present new capacity upper bounds for the discrete-time Poisson channel with no dark current and an Average-Power Constraint. These bounds are a simple consequence of techniques developed by one of the authors for the seemingly unrelated problem of upper bounding the capacity of binary deletion and repetition channels. Previously, the best known capacity upper bound in the regime where the Average-Power Constraint does not approach zero was due to Martinez (JOSA B, 2007), which we re-derive as a special case of our framework. Furthermore, we instantiate our framework to obtain a closed-form bound that noticeably improves the result of Martinez everywhere.
Yoshinari Takeishi - One of the best experts on this subject based on the ideXlab platform.
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An improved analysis of least squares superposition codes with bernoulli dictionary
Japanese Journal of Statistics and Data Science, 2019Co-Authors: Yoshinari Takeishi, Jun’ichi TakeuchiAbstract:For the additive white Gaussian noise channel with Average Power Constraint, sparse superposition codes (or sparse regression codes), proposed by Barron and Joseph in 2010, achieve the capacity. While the codewords of the original sparse superposition codes are made with a dictionary matrix drawn from a Gaussian distribution, we consider the case that it is drawn from a Bernoulli distribution. We show an improved upper bound on its block error probability with least squares decoding, which is fairly simplified and tighter bound than our previous result in 2014.
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An Improved Analysis of Least Squares Superposition Codes with Bernoulli Dictionary
arXiv: Information Theory, 2018Co-Authors: Yoshinari Takeishi, Junichi TakeuchiAbstract:For the additive white Gaussian noise channel with Average Power Constraint, sparse superposition codes, proposed by Barron and Joseph in 2010, achieve the capacity. While the codewords of the original sparse superposition codes are made with a dictionary matrix drawn from a Gaussian distribution, we consider the case that it is drawn from a Bernoulli distribution. We show an improved upper bound on its block error probability with least squares decoding, which is fairly simplified and tighter bound than our previous result in 2014.
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an improved upper bound on block error probability of least squares superposition codes with unbiased bernoulli dictionary
International Symposium on Information Theory, 2016Co-Authors: Yoshinari Takeishi, Junichi TakeuchiAbstract:For the additive white Gaussian noise channel with Average Power Constraint, it is shown that sparse superposition codes, proposed by Barron and Joseph in 2010, achieve the capacity. We study the upper bounds on its block error probability with least squares decoding when a dictionary with which we make codewords is drawn from an unbiased Bernoulli distribution. We improve the upper bounds shown by Takeishi et.al. in 2014 with fairly simplified form.
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ISIT - An improved upper bound on block error probability of least squares superposition codes with unbiased Bernoulli dictionary
2016 IEEE International Symposium on Information Theory (ISIT), 2016Co-Authors: Yoshinari Takeishi, Junichi TakeuchiAbstract:For the additive white Gaussian noise channel with Average Power Constraint, it is shown that sparse superposition codes, proposed by Barron and Joseph in 2010, achieve the capacity. We study the upper bounds on its block error probability with least squares decoding when a dictionary with which we make codewords is drawn from an unbiased Bernoulli distribution. We improve the upper bounds shown by Takeishi et.al. in 2014 with fairly simplified form.
