Vector Flows That Compute the Capacity of Discrete Memoryless Channels

One of the fundamental problems of information theory, since its foundation by C. Shannon, has been the computation of the capacity of a discrete memoryless channel, a quantity expressing the maximum rate at which information can travel through the channel. In this paper, we investigate the properti...

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Main Authors: Guglielmo Beretta, Marcello Pelillo
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Entropy
Subjects:
Online Access:https://www.mdpi.com/1099-4300/27/4/362
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author Guglielmo Beretta
Marcello Pelillo
author_facet Guglielmo Beretta
Marcello Pelillo
author_sort Guglielmo Beretta
collection DOAJ
description One of the fundamental problems of information theory, since its foundation by C. Shannon, has been the computation of the capacity of a discrete memoryless channel, a quantity expressing the maximum rate at which information can travel through the channel. In this paper, we investigate the properties of a novel approach to computing the capacity, based on a continuous-time dynamical system. Interestingly, the proposed dynamical system can be regarded as a continuous-time version of the classical Blahut–Arimoto algorithm, and we can prove that the former shares with the latter an exponential rate of convergence if certain conditions are met. Moreover, a circuit design is presented to implement the dynamics, hence enabling analog computation to estimate the capacity.
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spelling doaj-art-9a7c402fc06b49018eff35f12b80ddf92025-08-20T03:13:51ZengMDPI AGEntropy1099-43002025-03-0127436210.3390/e27040362Vector Flows That Compute the Capacity of Discrete Memoryless ChannelsGuglielmo Beretta0Marcello Pelillo1DAIS, Università Ca’ Foscari di Venezia, Via Torino 155, 30170 Venezia, ItalyDAIS, Università Ca’ Foscari di Venezia, Via Torino 155, 30170 Venezia, ItalyOne of the fundamental problems of information theory, since its foundation by C. Shannon, has been the computation of the capacity of a discrete memoryless channel, a quantity expressing the maximum rate at which information can travel through the channel. In this paper, we investigate the properties of a novel approach to computing the capacity, based on a continuous-time dynamical system. Interestingly, the proposed dynamical system can be regarded as a continuous-time version of the classical Blahut–Arimoto algorithm, and we can prove that the former shares with the latter an exponential rate of convergence if certain conditions are met. Moreover, a circuit design is presented to implement the dynamics, hence enabling analog computation to estimate the capacity.https://www.mdpi.com/1099-4300/27/4/362analog computationcapacityconvex optimizationdiscrete memoryless channeldynamical systemsmutual information
spellingShingle Guglielmo Beretta
Marcello Pelillo
Vector Flows That Compute the Capacity of Discrete Memoryless Channels
Entropy
analog computation
capacity
convex optimization
discrete memoryless channel
dynamical systems
mutual information
title Vector Flows That Compute the Capacity of Discrete Memoryless Channels
title_full Vector Flows That Compute the Capacity of Discrete Memoryless Channels
title_fullStr Vector Flows That Compute the Capacity of Discrete Memoryless Channels
title_full_unstemmed Vector Flows That Compute the Capacity of Discrete Memoryless Channels
title_short Vector Flows That Compute the Capacity of Discrete Memoryless Channels
title_sort vector flows that compute the capacity of discrete memoryless channels
topic analog computation
capacity
convex optimization
discrete memoryless channel
dynamical systems
mutual information
url https://www.mdpi.com/1099-4300/27/4/362
work_keys_str_mv AT guglielmoberetta vectorflowsthatcomputethecapacityofdiscretememorylesschannels
AT marcellopelillo vectorflowsthatcomputethecapacityofdiscretememorylesschannels