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A new approach for upper bounding the channel reliability function using the code spectrum is described. It allows to treat in a unified way both a low and a high rate cases. In particular, the earlier known upper bounds are improved, and a…

Information Theory · Computer Science 2007-07-16 Marat V. Burnashev

We derive a new upper bound on the reliability function for channel coding over discrete memoryless channels. Our bounding technique relies on two main elements: (i) adding an auxiliary genie-receiver that reveals to the original receiver a…

Information Theory · Computer Science 2022-09-05 Anelia Somekh-Baruch

We develop a novel framework for proving converse theorems for channel coding, which is based on the analysis technique of multicast transmission with an additional auxiliary receiver, which serves as a genie to the original receiver. The…

Information Theory · Computer Science 2022-09-02 Anelia Somekh-Baruch

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…

Information Theory · Computer Science 2025-09-15 Adeel Mahmood , Aaron B. Wagner

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…

Information Theory · Computer Science 2017-02-07 Yasutada Oohama

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…

Information Theory · Computer Science 2021-11-29 Antonino Favano , Marco Ferrari , Maurizio Magarini , Luca Barletta

An information measure based on fractional partitions of a set is used to derive a general dependence balance inequality for communication. This inequality is used to obtain new upper bounds on reliable and secret rates for multiterminal…

Information Theory · Computer Science 2026-04-14 Amin Gohari , Gerhard Kramer

We address the problem of bounding below the probability of error under maximum likelihood decoding of a binary code with a known distance distribution used on a binary symmetric channel. An improved upper bound is given for the maximum…

Information Theory · Computer Science 2007-07-16 Alexander Barg , Andrew McGregor

This article characterizes new boundary points on the capacity region of certain classes of more capable broadcast channels (BC) with uni-directional cooperation from the stronger to the weaker receiver. The new boundary points are achieved…

Information Theory · Computer Science 2026-01-14 Nicolas Le Gouic , Yossef Steinberg , Michèle Wigger

The problem of broadcasting a pair of correlated Gaussian sources using optimal separate source and channel codes is studied. Considerable performance gains over previously known separate source-channel schemes are observed. Although…

Information Theory · Computer Science 2011-05-10 Yang Gao , Ertem Tuncel

The sphere-packing bound $E_{sp}(R)$ bounds the reliability function for fixed-length block-codes. For symmetric channels, it remains a valid bound even when strictly causal noiseless feedback is allowed from the decoder to the encoder. To…

Information Theory · Computer Science 2007-07-13 Anant Sahai

We find the maximum rate achievable in the private communication over a bosonic quantum channel with a fully Gaussian protocol based on optimal single-mode Gaussian measurements. This rate establishes a lower bound on the secret rate…

Quantum Physics · Physics 2025-12-18 Giuseppe Ortolano , Stefano Pirandola , Leonardo Banchi

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…

Information Theory · Computer Science 2015-03-19 Chong Li , Nicola Elia

The reliability function gives the rate of exponential convergence to zero of the error probability in a communication channel. In this paper bounds for the reliability function of a quantum pure state channel are given, reminiscent of the…

Quantum Physics · Physics 2008-02-03 M. V. Burnashev , A. S. Holevo

In this paper, we investigate the additive Gaussian noise channel with noisy feedback. We consider the setup of linear coding of the feedback information and Gaussian signaling of the message (i.e. Cover-Pombra Scheme). Then, we derive the…

Information Theory · Computer Science 2015-03-19 Chong Li , Nicola Elia

We firstly extend the interpretation of feedback communication over stationary finite dimensional Gaussian channels as feedback control systems by showing that, the problem of finding stabilizing feedback controllers with maximal reliable…

Information Theory · Computer Science 2015-12-01 Chong Li , Nicola Elia

The problem of sending two correlated vector Gaussian sources over a bandwidth-matched two-user scalar Gaussian broadcast channel is studied in this work, where each receiver wishes to reconstruct its target source under a covariance…

Information Theory · Computer Science 2015-03-11 Lin Song , Jun Chen , Chao Tian

This paper investigates the maximal secrecy rate over a wiretap channel subject to reliability and secrecy constraints at a given blocklength. New achievability and converse bounds are derived, which are shown to be tighter than existing…

Information Theory · Computer Science 2016-01-25 Wei Yang , Rafael F. Schaefer , H. Vincent Poor

Lossy transmission over a relay channel in which the relay has access to correlated side information is considered. First, a joint source-channel decode-and-forward scheme is proposed for general discrete memoryless sources and channels.…

Information Theory · Computer Science 2016-11-17 Deniz Gunduz , Elza Erkip , Andrea J. Goldsmith , H. Vincent Poor

Spectral approximation and variational inducing learning for the Gaussian process are two popular methods to reduce computational complexity. However, in previous research, those methods always tend to adopt the orthonormal basis functions,…

Machine Learning · Statistics 2021-07-15 Wenqi Fang , Guanlin Wu , Jingjing Li , Zheng Wang , Jiang Cao , Yang Ping
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