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The capacity-achieving input distribution of the discrete-time, additive white Gaussian noise (AWGN) channel with an amplitude constraint is discrete and seems difficult to characterize explicitly. A dual capacity expression is used to…

信息论 · 计算机科学 2015-11-30 Andrew Thangaraj , Gerhard Kramer , Georg Bocherer

The capacity of time-varying channels with periodic feedback at the transmitter is evaluated. It is assumed that the channel state information is perfectly known at the receiver and is fed back to the transmitter at the regular…

信息论 · 计算机科学 2007-07-13 Mehdi Ansari Sadrabadi , Mohammad Ali Maddah-Ali , Amir K. Khandani

In this paper, the capacity of the additive white Gaussian noise (AWGN) channel, affected by time-varying Wiener phase noise is investigated. Tight upper and lower bounds on the capacity of this channel are developed. The upper bound is…

信息论 · 计算机科学 2015-09-22 M. Reza Khanzadi , Rajet Krishnan , Johan Söder , Thomas Eriksson

The capacity of a deterministic multiple-input multiple-output (MIMO) channel under the peak and average power constraints is investigated. For the identity channel matrix, the approach of Shamai et al. is generalized to the higher…

信息论 · 计算机科学 2016-09-29 Borzoo Rassouli , Bruno Clerckx

The capacity regions of vector, or multiple-input multiple-output, Gaussian interference channels are established for very strong interference and aligned strong interference. Furthermore, the sum-rate capacities are established for Z…

信息论 · 计算机科学 2016-11-17 Xiaohu Shang , Biao Chen , Gerhard Kramer , H. Vincent Poor

This paper studies the capacity of an $n$-dimensional vector Gaussian noise channel subject to the constraint that an input must lie in the ball of radius $R$ centered at the origin. It is known that in this setting the optimizing input…

信息论 · 计算机科学 2018-09-25 Alex Dytso , Mert Al , H. Vincent Poor , Shlomo Shamai

A novel technique to optimize the input distribution and compute a lower bound for the capacity of the nonlinear optical fiber channel is proposed. The technique improves previous bounds obtained with the additive white Gaussian noise…

信息论 · 计算机科学 2021-06-09 Stella Civelli , Enrico Forestieri , Alexey Lotsmanov , Dmitry Razdoburdin , Marco Secondini

We study memoryless, discrete time, matrix channels with additive white Gaussian noise and input power constraints of the form $Y_i = \sum_j H_{ij} X_j + Z_i$, where $Y_i$ ,$X_j$ and $Z_i$ are complex, $i=1..m$, $j=1..n$, and $H$ is a…

数据分析、统计与概率 · 物理学 2007-05-23 Anirvan Mayukh Sengupta , Partha Pratim Mitra

This paper derives upper and lower bounds on the capacity of the multiple-input single-output free-space optical intensity channel with signal-independent additive Gaussian noise subject to both an average-intensity and a peak-intensity…

信息论 · 计算机科学 2017-10-26 Stefan M. Moser , Ligong Wang , Michèle Wigger

In this article, we are proposing a closed-form solution for the capacity of the single quantum channel. The Gaussian distributed input has been considered for the analytical calculation of the capacity. In our previous couple of papers, we…

信息论 · 计算机科学 2023-02-17 Mouli Chakraborty , Harun Siljak , Indrakshi Dey , Nicola Marchetti

We consider the additive white Gaussian noise channels. We prove that the error probability of decoding tends to one exponentially for rates above the capacity and derive the optimal exponent function. We shall demonstrate that the…

信息论 · 计算机科学 2017-02-07 Yasutada Oohama

New sufficient conditions for a vector Gaussian interference channel to achieve the sum-rate capacity by treating interference as noise are derived, which generalize the existing results. More concise conditions for…

信息论 · 计算机科学 2016-11-17 Xiaohu Shang , H. Vincent Poor

Information transmission over discrete-time channels with memoryless additive noise obeying a Cauchy, rather than Gaussian, distribution, are studied. The channel input satisfies an average power constraint. Upper and lower bounds to such…

信息论 · 计算机科学 2024-11-19 Shuqin Pang , Wenyi Zhang

Upper bounds on the capacity of vector Gaussian channels affected by fading are derived under peak amplitude constraints at the input. The focus is on constraint regions that can be decomposed in a Cartesian product of sub-regions. This…

信息论 · 计算机科学 2022-07-05 Antonino Favano , Marco Ferrari , Maurizio Magarini , Luca Barletta

In this paper, we revisit the problem of finding the average capacity of the Gaussian feedback channel. First, we consider the problem of finding the average capacity of the analog Gaussian noise channel where the noise has an arbitrary…

信息论 · 计算机科学 2019-01-24 Ather Gattami

The most natural way to describe an information-carrying system containing a specific noise is an additive white Gaussian-noise (AWGN) channel. In bosonic quantum systems (especially the Gaussian case), although the classical information…

量子物理 · 物理学 2019-05-14 Kabgyun Jeong , Hun Hee Lee , Youngrong Lim

The capacity of a complex and discrete-time memoryless additive white Gaussian noise (AWGN) channel under three constraints, namely, input average power, input amplitude and output delivered power is studied. The output delivered power…

信息论 · 计算机科学 2020-04-27 Morteza Varasteh , Borzoo Rassouli , Bruno Clerckx

We study the amplitude-constrained additive white Gaussian noise channel. It is well known that the capacity-achieving input distribution for this channel is discrete and supported on finitely many points. The best known bounds show that…

信息论 · 计算机科学 2026-03-26 Haiyang Wang , Luca Barletta , Alex Dytso

We consider an additive Gaussian channel with additive Gaussian noise feedback. We derive an upper bound on the n-block capacity (defined by Cover [1]). It is shown that this upper bound can be obtained by solving a convex optimization…

信息论 · 计算机科学 2015-03-19 Chong Li , Nicola Elia

Recent outer bounds on the capacity region of Gaussian interference channels are generalized to $m$-user channels with $m>2$ and asymmetric powers and crosstalk coefficients. The bounds are again shown to give the sum-rate capacity for…

信息论 · 计算机科学 2008-01-16 Xiaohu Shang , Gerhard Kramer , Biao Chen
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