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Genetic Algorithms to Maximize the Relevant Mutual Information in Communication Receivers

Lewandowsky, Jan ; Dongare, Sumedh Jitendra ; Martín Lima, Rocío ; Adrat, Marc ; Schrammen, Matthias ; Jax, Peter (2024)
Genetic Algorithms to Maximize the Relevant Mutual Information in Communication Receivers.
In: Electronics, 2021, 10 (12)
doi: 10.26083/tuprints-00019542
Article, Secondary publication, Publisher's Version

Copyright Information: CC BY 4.0 International - Creative Commons, Attribution.

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Item Type: Article
Type of entry: Secondary publication
Title: Genetic Algorithms to Maximize the Relevant Mutual Information in Communication Receivers
Language: English
Date: 15 January 2024
Place of Publication: Darmstadt
Year of primary publication: 2021
Place of primary publication: Basel
Publisher: MDPI
Journal or Publication Title: Electronics
Volume of the journal: 10
Issue Number: 12
Collation: 21 Seiten
DOI: 10.26083/tuprints-00019542
Corresponding Links:
Origin: Secondary publication DeepGreen

The preservation of relevant mutual information under compression is the fundamental challenge of the information bottleneck method. It has many applications in machine learning and in communications. The recent literature describes successful applications of this concept in quantized detection and channel decoding schemes. The focal idea is to build receiver algorithms intended to preserve the maximum possible amount of relevant information, despite very coarse quantization. The existent literature shows that the resulting quantized receiver algorithms can achieve performance very close to that of conventional high-precision systems. Moreover, all demanding signal processing operations get replaced with lookup operations in the considered system design. In this paper, we develop the idea of maximizing the preserved relevant information in communication receivers further by considering parametrized systems. Such systems can help overcome the need of lookup tables in cases where their huge sizes make them impractical. We propose to apply genetic algorithms which are inspired from the natural evolution of the species for the problem of parameter optimization. We exemplarily investigate receiver-sided channel output quantization and demodulation to illustrate the notable performance and the flexibility of the proposed concept.

Uncontrolled Keywords: information bottleneck, mutual information, genetic algorithms, machine learning
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-195429
Additional Information:

This article belongs to the Special Issue Selected Papers from 14th International Conference on Signal Processing and Communication Systems.

This article is an extended and improved version of our paper published in: Lewandowsky, J.; Dongare, S.J.; Adrat, M.; Schrammen, M.; Jax, P. Optimizing parametrized information bottleneck compression mappings with genetic algorithms. In Proceedings of the 14th International Conference on Signal Processing and Communication Systems (ICSPCS’2020), Adelaide, Australia, 14–16 December 2020; pp. 1–8.

Classification DDC: 600 Technology, medicine, applied sciences > 621.3 Electrical engineering, electronics
Divisions: 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Communications Engineering
Date Deposited: 15 Jan 2024 13:34
Last Modified: 13 Mar 2024 07:01
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/19542
PPN: 51617858X
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