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相关论文: On the Capacity of the Peak Power Constrained Vect…

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This paper studies secrecy-capacity of an $n$-dimensional Gaussian wiretap channel under the peak-power constraint. This work determines the largest peak-power constraint $\bar{\mathsf{R}}_n$ such that an input distribution uniformly…

信息论 · 计算机科学 2022-02-02 Antonino Favano , Luca Barletta , Alex Dytso

This paper studies secrecy-capacity of an $n$-dimensional Gaussian wiretap channel under a peak-power constraint. This work determines the largest peak-power constraint $\bar{\mathsf{R}}_n$ such that an input distribution uniformly…

信息论 · 计算机科学 2023-05-17 Antonino Favano , Luca Barletta , Alex Dytso

The capacity of multiple-input multiple-output additive white Gaussian noise channels is investigated under peak amplitude constraints on the norm of the input vector. New insights on the capacity-achieving input distribution are presented.…

信息论 · 计算机科学 2021-05-06 Antonino Favano , Marco Ferrari , Maurizio Magarini , Luca Barletta

This paper studies an $n$-dimensional additive Gaussian noise channel with a peak-power-constrained input. It is well known that, in this case, when $n=1$ the capacity-achieving input distribution is discrete with finitely many mass points,…

信息论 · 计算机科学 2019-11-18 Alex Dytso , Semih Yagli , H. Vincent Poor , Shlomo Shamai

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

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

In this paper, we delve into the capacity problem of additive vertically-drifted first arrival position noise channel, which models a communication system where the position of molecules is harnessed to convey information. Drawing…

信息论 · 计算机科学 2023-05-24 Yun-Feng Lo , Yen-Chi Lee , Min-Hsiu Hsieh

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

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

We consider channel coding for Gaussian channels with the recently introduced mean and variance cost constraints. Through matching converse and achievability bounds, we characterize the optimal first- and second-order performance. The main…

信息论 · 计算机科学 2025-09-15 Adeel Mahmood , Aaron B. Wagner

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

An upper bound on the capacity of multiple-input multiple-output (MIMO) Gaussian fading channels is derived under peak amplitude constraints. The upper bound is obtained borrowing concepts from convex geometry and it extends to MIMO…

信息论 · 计算机科学 2021-11-29 Antonino Favano , Marco Ferrari , Maurizio Magarini , Luca Barletta

The capacity of the point-to-point vector Gaussian channel under the peak power constraint is not known in general. This paper considers a simpler scenario in which the input signal vector is forced to have a constant envelope (or norm).…

信息论 · 计算机科学 2016-05-13 Borzoo Rassouli , Bruno Clerckx

This paper adds to the understanding of the capacity region of the Gaussian interference channel. To this end, the capacity region of the one-sided Gaussian interference channel is first fully characterized. This is accomplished by…

信息论 · 计算机科学 2015-09-22 Mojtaba Vaezi , H. Vincent Poor

This work considers a Poisson noise channel with an amplitude constraint. It is well-known that the capacity-achieving input distribution for this channel is discrete with finitely many points. We sharpen this result by introducing upper…

信息论 · 计算机科学 2021-07-30 Alex Dytso , Luca Barletta , Shlomo Shamai

A new outer bound on the capacity region of Gaussian interference channels is developed. The bound combines and improves existing genie-aided methods and is shown to give the sum-rate capacity for noisy interference as defined in this…

信息论 · 计算机科学 2016-11-18 Xiaohu Shang , Gerhard Kramer , Biao Chen

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

A multi-input multi-output (MIMO) Gaussian channel with two transmit antennas and two receive antennas is studied that is subject to an input peak-power constraint. The capacity and the capacity-achieving input distribution are unknown in…

信息论 · 计算机科学 2024-01-31 Alex Dytso , Luca Barletta , Gerhard Kramer

We develop a new method for showing the optimality of the Gaussian distribution in multiterminal information theory problems. As an application of this method we show that Marton's inner bound achieves the capacity of the vector Gaussian…

信息论 · 计算机科学 2012-02-02 Yanlin Geng , Chandra Nair

The full solution of the optimization problem giving the Gaussian capacity of the single-mode fiducial Gaussian quantum channel is provided. Since it was shown that the Gaussian capacity of an arbitrary (phase-sensitive or insensitive)…

量子物理 · 物理学 2016-09-19 Joachim Schäfer , Evgueni Karpov , Oleg V. Pilyavets , Nicolas J. Cerf
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