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Motivated by the need for communication-efficient distributed learning, we investigate the method for compressing a unit norm vector into the minimum number of bits, while still allowing for some acceptable level of distortion in recovery.…

Information Theory · Computer Science 2024-02-06 Heng Zhu , Avishek Ghosh , Arya Mazumdar

In this paper, we consider a scalar-help-vector source coding problem for $L+1$ correlated Gaussian memoryless sources. We deal with the case where $L$ encoders observe noisy linear combinations of $K$ correlated Gaussian scalar sources…

Information Theory · Computer Science 2019-11-14 C. Deng , S. Wu , Q. Zhang

Representing a continuous-time signal by a set of samples is a classical problem in signal processing. We study this problem under the additional constraint that the samples are quantized or compressed in a lossy manner under a limited…

Information Theory · Computer Science 2018-04-12 Alon Kipnis , Yonina C. Eldar , Andrea J. Goldsmith

A new source model, which consists of an intrinsic state part and an extrinsic observation part, is proposed and its information-theoretic characterization, namely its rate-distortion function, is defined and analyzed. Such a source model…

Information Theory · Computer Science 2022-06-02 Jiakun Liu , Shuo Shao , Wenyi Zhang , H. Vincent Poor

We consider the transmission of a memoryless bivariate Gaussian source over a two-user additive Gaussian multiple-access channel with unidirectional conferencing encoders. Here, prior to each transmission block, Encoder 1, which observes…

Information Theory · Computer Science 2015-12-31 Shraga I. Bross , Yaron Laufer

Many information systems employ lossy compression as a crucial intermediate stage among other processing components. While the important distortion is defined by the system's input and output signals, the compression usually ignores the…

Information Theory · Computer Science 2018-05-14 Yehuda Dar , Michael Elad , Alfred M. Bruckstein

We investigate the optimal performance of dense sensor networks by studying the joint source-channel coding problem. The overall goal of the sensor network is to take measurements from an underlying random process, code and transmit those…

Information Theory · Computer Science 2007-07-13 Nan Liu , Sennur Ulukus

An information-spectrum approach is applied to solve the multiterminal source coding problem for correlated general sources, where sources may be nonstationary and/or nonergodic, and the distortion measure is arbitrary and may be…

Information Theory · Computer Science 2007-07-13 Shengtian Yang , Peiliang Qiu

Coded source compression, also known as source compression with helpers, has been a major variant of distributed source compression, but has hitherto received little attention in the quantum regime. This work treats and solves the…

Information Theory · Computer Science 2019-03-08 Min-Hsiu Hsieh , Shun Watanabe

The Gaussian multiple-input multiple-output (MIMO) multi-receiver wiretap channel is studied in this paper. The base station broadcasts confidential messages to K intended users while keeping the messages secret from an eavesdropper. The…

Information Theory · Computer Science 2022-05-16 Yue Qi , Mojtaba Vaezi

A distortion-noise profile is a function indicating the maximum allowed source distortion value for each noise level in the channel. In this paper, the minimum energy required to achieve a distortion noise profile is studied for Gaussian…

Information Theory · Computer Science 2019-11-28 Mohammadamin Baniasadi , Ertem Tuncel

This paper considers the source coding problem with broadcast side information. The side information is sent to two receivers through a noisy broadcast channel. We provide an outer bound of the rate--distortion--bandwidth (RDB) quadruples…

Information Theory · Computer Science 2026-02-19 Yiqi Chen , Holger Boche , Marc Geitz

We study the problem of transmitting a source sample with minimum distortion over an infinite-bandwidth additive white Gaussian noise channel under an energy constraint. To that end, we construct a joint source--channel coding scheme using…

Information Theory · Computer Science 2024-10-28 Omri Lev , Anatoly Khina

We devise achievable encoding schemes for distributed source compression for computing inner products, symmetric matrix products, and more generally, square matrix products, which are a class of nonlinear transformations. To that end, our…

Information Theory · Computer Science 2024-05-21 Derya Malak

We study the joint source channel coding problem of transmitting an analog source over a Gaussian channel in two cases - (i) the presence of interference known only to the transmitter and (ii) in the presence of side information known only…

Information Theory · Computer Science 2016-09-08 Makesh Pravin Wilson , Krishna Narayanan , Giuseppe Caire

We consider a worst-case asymmetric distributed source coding problem where an information sink communicates with $N$ correlated information sources to gather their data. A data-vector $\bar{x} = (x_1, ..., x_N) \sim {\mathcal P}$ is…

Information Theory · Computer Science 2013-01-03 Samar Agnihotri , Rajesh Venkatachalapathy

We consider a rate-distortion version of the quantum state redistribution task, where the error of the decoded state is judged via an additive distortion measure; it thus constitutes a quantum generalisation of the classical Wyner-Ziv…

Quantum Physics · Physics 2025-04-11 Zahra Baghali Khanian , Andreas Winter

We begin by presenting a simple lossy compressor operating at near-zero rate: The encoder merely describes the indices of the few maximal source components, while the decoder's reconstruction is a natural estimate of the source components…

Information Theory · Computer Science 2016-03-09 Albert No , Tsachy Weissman

This paper is concerned with an inverse random source problem for the three-dimensional time-harmonic Maxwell equations. The source is assumed to be a centered complex-valued Gaussian vector field with correlated components, and its…

Analysis of PDEs · Mathematics 2020-02-21 Peijun Li , Xu Wang

The Gauss-Markov source produces $U_i = aU_{i-1} + Z_i$ for $i\geq 1$, where $U_0 = 0$, $|a|<1$ and $Z_i\sim\mathcal{N}(0, \sigma^2)$ are i.i.d. Gaussian random variables. We consider lossy compression of a block of $n$ samples of the…

Information Theory · Computer Science 2019-09-26 Peida Tian , Victoria Kostina
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