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Bayesian estimation of Gaussian graphical models has proven to be challenging because the conjugate prior distribution on the Gaussian precision matrix, the G-Wishart distribution, has a doubly intractable partition function. Recent…

Neurons and Cognition · Quantitative Biology 2014-09-10 Max Hinne , Alex Lenkoski , Tom Heskes , Marcel van Gerven

The mutual information of bit-interleaved coded modulation (BICM) systems, sometimes called the BICM capacity, is investigated at low signal-to-noise ratio (SNR), i.e., in the wideband regime. A new linear transform that depends on bits'…

Information Theory · Computer Science 2014-04-23 Erik Agrell , Alex Alvarado

In the present paper, a simplified two-dimensional magnetic recording (TDMR) channel model is proposed in order to capture the qualitative features of writing and read-back processes of TDMR systems.The proposed channel model incorporates…

Information Theory · Computer Science 2015-04-01 Tadashi Wadayama

The transmission of digital data is one of the principal tasks in modern wireless communication. Classically, the communication channel consists of one transmitter and one receiver; however, due to the constantly increasing demand in higher…

Information Theory · Computer Science 2015-01-08 Andrei Osipov

The root raised-cosine pulse commonly used in linear digital modulations yields exactly two intersymbol interference components from the preceding and the subsequent data symbols, provided that the roll-off factor is $100\%$ and the…

Information Theory · Computer Science 2023-01-05 Pavel Loskot

Hardware distortions (HWD) render drastic effects on the performance of communication systems. They are recently proven to bear asymmetric signatures; and hence can be efficiently mitigated using improper Gaussian signaling (IGS), thanks to…

Signal Processing · Electrical Eng. & Systems 2020-09-14 Sidrah Javed , Ahmed Elzanaty , Osama Amin , Basem Shihada , Mohamed-Slim Alouini

Generalized spatial modulation (GSM) is a spectral-efficient technique used in multiple-input multiple-output (MIMO) wireless communications when the number of radio frequency chains at the transmitter is less than the number of transmit…

Information Theory · Computer Science 2020-08-11 Lakshit Singla , Lakshmi Natarajan

A probabilistic shaping method for multi-level coding (MLC) is presented, where the transmitted symbols are forced to have a shaped non-uniform distribution. It is shown that shaping only a single bit-level suffices to compensate for most…

Information Theory · Computer Science 2018-12-20 Onurcan İşcan , Ronald Böhnke , Wen Xu

This paper considers belief propagation algorithm over pair-wise graphical models to develop low complexity, iterative multiple-input multiple-output (MIMO) detectors. The pair-wise graphical model is a bipartite graph where a pair of…

Information Theory · Computer Science 2013-04-09 Seokhyun Yoon , Chan-Byoung Chae

An online shaping technique for high performance communication over Gaussian channels with Inter-Symbol Interference (ISI) and receiver Analog to Digital Converter (ADC) noise is presented. The technique uses online transmitter precoding…

Information Theory · Computer Science 2020-10-15 Or Levi , Dan Raphaeli

The construction of optimal non-uniform mappings for discrete input memoryless channels (DIMCs) is investigated. An efficient algorithm to find optimal mappings is proposed and the rate by which a target distribution is approached is…

Information Theory · Computer Science 2014-07-09 Georg Böcherer

The Yule--Simon distribution has been out of the radar of the Bayesian community, so far. In this note, we propose an explicit Gibbs sampling scheme when a Gamma prior is chosen for the shape parameter. The performance of the algorithm is…

Methodology · Statistics 2017-07-04 Fabrizio Leisen , Luca Rossini , Cristiano Villa

The paper introduces a new technique for compressing Binary Decision Diagrams in those cases where random access is not required. Using this technique, compression and decompression can be done in linear time in the size of the BDD and…

Artificial Intelligence · Computer Science 2008-12-18 Esben Rune Hansen , S. Srinivasa Rao , Peter Tiedemann

This paper introduces a new quantization scheme for real and complex Grassmannian sources. The proposed approach relies on a structured codebook based on a geometric construction of a collection of bent grids defined from an initial mesh on…

Information Theory · Computer Science 2016-10-27 Alexis Decurninge , Maxime Guillaud

The usual setting for learning the structure and parameters of a graphical model assumes the availability of independent samples produced from the corresponding multivariate probability distribution. However, for many models the mixing time…

Machine Learning · Computer Science 2022-10-13 Arkopal Dutt , Andrey Y. Lokhov , Marc Vuffray , Sidhant Misra

Scalar quantization and probabilistic shaping are applied to the distributed source coding of Gaussian sources, with mean-square error distortion. A coding scheme with a modulo interval, dithering, and truncated Gaussian shaping is shown to…

Information Theory · Computer Science 2026-04-21 Muhammed Yusuf Sener , Gerhard Kramer , Shlomo Shamai , Ronald Böhnke , Wen Xu

In this paper, we present a Bayesian channel estimation algorithm for multicarrier receivers based on pilot symbol observations. The inherent sparse nature of wireless multipath channels is exploited by modeling the prior distribution of…

Machine Learning · Statistics 2013-03-07 Niels Lovmand Pedersen , Carles Navarro Manchón Bernard Henri Fleury

We introduce the problem of communication with partial information, where there is an asymmetry between the transmitter and the receiver codebooks. Practical applications of the proposed setup include the robust signal hashing problem…

Information Theory · Computer Science 2009-10-29 Onur Ozyesil , M. Kivanc Mihcak , Yucel Altug

Probabilistic shaping (PS) has been widely studied and applied to optical fiber communications. The encoder of PS expends the number of bit slots and controls the probability distribution of channel input symbols. Not only studies focused…

Information Theory · Computer Science 2024-01-25 Tsuyoshi Yoshida , Koji Igarashi , Magnus Karlsson , Erik Agrell

We propose a Bayesian approximate inference method for learning the dependence structure of a Gaussian graphical model. Using pseudo-likelihood, we derive an analytical expression to approximate the marginal likelihood for an arbitrary…

Machine Learning · Statistics 2017-04-13 Janne Leppä-aho , Johan Pensar , Teemu Roos , Jukka Corander