Neural Estimation of Multi-User Capacity Regions

Bashar Huleihel*, Dor Tsur, Ziv Aharoni, Oron Sabag, Haim H. Permuter

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


In this paper, we introduce a data-driven methodology for estimating capacity regions of continuous channels in multi-user communication systems. Computing capacity regions is a long standing open problem, even in simple communication scenarios. Nevertheless, it is often possible to represent their capacity regions as the limit of an optimization problem (a multi-letter expression). In many cases, these multi-letter expressions can be expressed in terms of directed information (DI) rates. Accordingly, our approach utilizes neural networks to estimate capacity regions, leveraging the recent introduction of the directed information neural estimator (DINE). The main idea of our methodology involves training DINE-based models using samples of channel inputs and outputs, and using these models to estimate the DI rate terms that are intrinsic to the studied capacity region. To estimate the capacity region rates, we optimize the DI rates over the involved input distributions which are parameterized by a neural distribution transformer (NDT), and execute an alternating maximization procedure between the NDT models and DINE-based models until convergence is achieved. The methodology is suitable for the case where the channel is treated as a "black-box"and the designer can only gather observations of its inputs and outputs, lacking any knowledge of the explicit channel model. The performance of our proposed algorithm is shown via several well-known settings, including the Gaussian two-way channel and the two-user Gaussian multiple-access channel with and without feedback.

Original languageAmerican English
Title of host publication2023 IEEE International Symposium on Information Theory, ISIT 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665475549
StatePublished - 2023
Event2023 IEEE International Symposium on Information Theory, ISIT 2023 - Taipei, Taiwan, Province of China
Duration: 25 Jun 202330 Jun 2023

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8095


Conference2023 IEEE International Symposium on Information Theory, ISIT 2023
Country/TerritoryTaiwan, Province of China

Bibliographical note

Publisher Copyright:
© 2023 IEEE.


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