A Deep Variational Approach to Multiterminal Joint Source-Channel Coding Based on Information Bottleneck Principle
In this article, we concentrate on a generic multiterminal joint source-channel coding scenario, appearing in a wide variety of real-world applications. Specifically, several noisy observations from a source signal must be compressed at some intermediate nodes, before getting forwarded over multiple...
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of the Communications Society |
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| Online Access: | https://ieeexplore.ieee.org/document/11005413/ |
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| author | Shayan Hassanpour Matthias Hummert Dirk Wubben Armin Dekorsy |
| author_facet | Shayan Hassanpour Matthias Hummert Dirk Wubben Armin Dekorsy |
| author_sort | Shayan Hassanpour |
| collection | DOAJ |
| description | In this article, we concentrate on a generic multiterminal joint source-channel coding scenario, appearing in a wide variety of real-world applications. Specifically, several noisy observations from a source signal must be compressed at some intermediate nodes, before getting forwarded over multiple error-prone and rate-limited channels towards a (remote) processing unit. The imperfections of the forward channels should be integrated into the design of (local) compressor units. By following the Information Bottleneck principle, the Mutual Information is selected here as the fidelity criterion, and a novel (data-driven) design approach is presented for two distinct types of processing flow/strategy at the remote unit. To that end, tractable objective functions are developed, together with the pertinent learning architectures, generalizing the concepts of Variational Auto-Encoders and (Distributed) Deep Variational Information Bottleneck for (remote) source coding to the context of distributed joint source-channel coding. Unlike the conventional approaches, the proposed schemes here work based upon a finite sample set, thereby obviating the call for full prior knowledge of the joint statistics of input signals. The effectiveness of these novel sample-based compression schemes is substantiated as well by a couple of simulations over typical transmission setups. |
| format | Article |
| id | doaj-art-c32e49cb262e4f2c87bf9119b8db9ef5 |
| institution | DOAJ |
| issn | 2644-125X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj-art-c32e49cb262e4f2c87bf9119b8db9ef52025-08-20T03:05:57ZengIEEEIEEE Open Journal of the Communications Society2644-125X2025-01-0164462447510.1109/OJCOMS.2025.357044611005413A Deep Variational Approach to Multiterminal Joint Source-Channel Coding Based on Information Bottleneck PrincipleShayan Hassanpour0https://orcid.org/0000-0003-2598-0415Matthias Hummert1https://orcid.org/0000-0003-2561-161XDirk Wubben2https://orcid.org/0000-0003-3854-6140Armin Dekorsy3https://orcid.org/0000-0002-5790-1470Department of Communications Engineering, University of Bremen, Bremen, GermanyDepartment of Communications Engineering, University of Bremen, Bremen, GermanyDepartment of Communications Engineering, University of Bremen, Bremen, GermanyDepartment of Communications Engineering, University of Bremen, Bremen, GermanyIn this article, we concentrate on a generic multiterminal joint source-channel coding scenario, appearing in a wide variety of real-world applications. Specifically, several noisy observations from a source signal must be compressed at some intermediate nodes, before getting forwarded over multiple error-prone and rate-limited channels towards a (remote) processing unit. The imperfections of the forward channels should be integrated into the design of (local) compressor units. By following the Information Bottleneck principle, the Mutual Information is selected here as the fidelity criterion, and a novel (data-driven) design approach is presented for two distinct types of processing flow/strategy at the remote unit. To that end, tractable objective functions are developed, together with the pertinent learning architectures, generalizing the concepts of Variational Auto-Encoders and (Distributed) Deep Variational Information Bottleneck for (remote) source coding to the context of distributed joint source-channel coding. Unlike the conventional approaches, the proposed schemes here work based upon a finite sample set, thereby obviating the call for full prior knowledge of the joint statistics of input signals. The effectiveness of these novel sample-based compression schemes is substantiated as well by a couple of simulations over typical transmission setups.https://ieeexplore.ieee.org/document/11005413/6Gauto-encodersdeep learninginformation bottleneckjoint source-channel coding |
| spellingShingle | Shayan Hassanpour Matthias Hummert Dirk Wubben Armin Dekorsy A Deep Variational Approach to Multiterminal Joint Source-Channel Coding Based on Information Bottleneck Principle IEEE Open Journal of the Communications Society 6G auto-encoders deep learning information bottleneck joint source-channel coding |
| title | A Deep Variational Approach to Multiterminal Joint Source-Channel Coding Based on Information Bottleneck Principle |
| title_full | A Deep Variational Approach to Multiterminal Joint Source-Channel Coding Based on Information Bottleneck Principle |
| title_fullStr | A Deep Variational Approach to Multiterminal Joint Source-Channel Coding Based on Information Bottleneck Principle |
| title_full_unstemmed | A Deep Variational Approach to Multiterminal Joint Source-Channel Coding Based on Information Bottleneck Principle |
| title_short | A Deep Variational Approach to Multiterminal Joint Source-Channel Coding Based on Information Bottleneck Principle |
| title_sort | deep variational approach to multiterminal joint source channel coding based on information bottleneck principle |
| topic | 6G auto-encoders deep learning information bottleneck joint source-channel coding |
| url | https://ieeexplore.ieee.org/document/11005413/ |
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