End-to-End Learning of Communication Systems with Novel Data-Efficient IIR Channel Identification

Roya Khanzadeh; Stefan Angerbauer; Andreas Springer; Werner Haselmayr

doi:10.1109/IEEECONF59524.2023.10476924

End-to-End Learning of Communication Systems with Novel Data-Efficient IIR Channel Identification

Abteilung für Nachrichtentechnik

Publikation: Beitrag in Buch/Bericht/Konferenzband › Konferenzbeitrag › Begutachtung

Abstract

In this paper, we introduce a novel end-to-end deep learning procedure for communication systems, which is data- efficient and capable of dealing with infinite memory length of communication channels. Therefore, as opposed to recent works, we utilize a low-complexity algorithm to identify the communication channel. The channel model is obtained purely from data and its output is differentiable with respect to its input, which is a basic requirement for gradient-based optimization of the auto-encoder neural network. We study the performance of the algorithm for a variety of challenging channels from different domains of communication engineering showing the broad applicability of the proposed approach.

Originalsprache	Englisch
Titel	Conference Record of the 57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023
Herausgeber*innen	Michael B. Matthews
Seiten	40-46
Seitenumfang	7
ISBN (elektronisch)	9798350325744
DOIs	https://doi.org/10.1109/IEEECONF59524.2023.10476924
Publikationsstatus	Veröffentlicht - Okt. 2023

Publikationsreihe

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)	1058-6393

Wissenschaftszweige

202038 Telekommunikation
202 Elektrotechnik, Elektronik, Informationstechnik
202037 Signalverarbeitung

JKU-Schwerpunkte

Digital Transformation

Zugriff auf Dokument

10.1109/IEEECONF59524.2023.10476924

Andere Dateien und Links

Verknüpfung zur Publikation in Scopus

JKU LIT SAL eSPML Lab
Baumgartner, S. (Forscher*in), Bognar, G. (Forscher*in), Hochreiter, S. (Forscher*in), Hofmarcher, M. (Forscher*in), Kovacs, P. (Forscher*in), Schmid, S. (Forscher*in), Shtainer, A. (Forscher*in), Springer, A. (Forscher*in), Wille, R. (Forscher*in) & Huemer, M. (Projektleiter*in)
01.07.2020 → 31.12.2023
Projekt: Anderes › Sonstiges Projekt

Dieses zitieren

Khanzadeh, R., Angerbauer, S., Springer, A., & Haselmayr, W. (2023). End-to-End Learning of Communication Systems with Novel Data-Efficient IIR Channel Identification. in M. B. Matthews (Hrsg.), Conference Record of the 57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023 (S. 40-46). (Conference Record - Asilomar Conference on Signals, Systems and Computers). https://doi.org/10.1109/IEEECONF59524.2023.10476924

Khanzadeh, Roya ; Angerbauer, Stefan ; Springer, Andreas et al. / End-to-End Learning of Communication Systems with Novel Data-Efficient IIR Channel Identification. Conference Record of the 57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023. Hrsg. / Michael B. Matthews. 2023. S. 40-46 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

@inproceedings{aba911b15bf846e38066ecd72482695d,

title = "End-to-End Learning of Communication Systems with Novel Data-Efficient IIR Channel Identification",

abstract = "In this paper, we introduce a novel end-to-end deep learning procedure for communication systems, which is data- efficient and capable of dealing with infinite memory length of communication channels. Therefore, as opposed to recent works, we utilize a low-complexity algorithm to identify the communication channel. The channel model is obtained purely from data and its output is differentiable with respect to its input, which is a basic requirement for gradient-based optimization of the auto-encoder neural network. We study the performance of the algorithm for a variety of challenging channels from different domains of communication engineering showing the broad applicability of the proposed approach.",

author = "Roya Khanzadeh and Stefan Angerbauer and Andreas Springer and Werner Haselmayr",

year = "2023",

month = oct,

doi = "10.1109/IEEECONF59524.2023.10476924",

language = "English",

series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",

pages = "40--46",

editor = "Matthews, \{Michael B.\}",

booktitle = "Conference Record of the 57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023",

}

Khanzadeh, R , Angerbauer, S , Springer, A & Haselmayr, W 2023, End-to-End Learning of Communication Systems with Novel Data-Efficient IIR Channel Identification. in MB Matthews (Hrsg.), Conference Record of the 57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023. Conference Record - Asilomar Conference on Signals, Systems and Computers, S. 40-46. https://doi.org/10.1109/IEEECONF59524.2023.10476924

End-to-End Learning of Communication Systems with Novel Data-Efficient IIR Channel Identification. / Khanzadeh, Roya ; Angerbauer, Stefan ; Springer, Andreas et al.
Conference Record of the 57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023. Hrsg. / Michael B. Matthews. 2023. S. 40-46 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

Publikation: Beitrag in Buch/Bericht/Konferenzband › Konferenzbeitrag › Begutachtung

TY - GEN

T1 - End-to-End Learning of Communication Systems with Novel Data-Efficient IIR Channel Identification

AU - Khanzadeh, Roya

AU - Angerbauer, Stefan

AU - Springer, Andreas

AU - Haselmayr, Werner

PY - 2023/10

Y1 - 2023/10

N2 - In this paper, we introduce a novel end-to-end deep learning procedure for communication systems, which is data- efficient and capable of dealing with infinite memory length of communication channels. Therefore, as opposed to recent works, we utilize a low-complexity algorithm to identify the communication channel. The channel model is obtained purely from data and its output is differentiable with respect to its input, which is a basic requirement for gradient-based optimization of the auto-encoder neural network. We study the performance of the algorithm for a variety of challenging channels from different domains of communication engineering showing the broad applicability of the proposed approach.

AB - In this paper, we introduce a novel end-to-end deep learning procedure for communication systems, which is data- efficient and capable of dealing with infinite memory length of communication channels. Therefore, as opposed to recent works, we utilize a low-complexity algorithm to identify the communication channel. The channel model is obtained purely from data and its output is differentiable with respect to its input, which is a basic requirement for gradient-based optimization of the auto-encoder neural network. We study the performance of the algorithm for a variety of challenging channels from different domains of communication engineering showing the broad applicability of the proposed approach.

UR - https://www.scopus.com/pages/publications/85190362569

U2 - 10.1109/IEEECONF59524.2023.10476924

DO - 10.1109/IEEECONF59524.2023.10476924

M3 - Conference proceedings

T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers

SP - 40

EP - 46

BT - Conference Record of the 57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023

A2 - Matthews, Michael B.

ER -

Khanzadeh R , Angerbauer S , Springer A , Haselmayr W. End-to-End Learning of Communication Systems with Novel Data-Efficient IIR Channel Identification. in Matthews MB, Hrsg., Conference Record of the 57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023. 2023. S. 40-46. (Conference Record - Asilomar Conference on Signals, Systems and Computers). doi: 10.1109/IEEECONF59524.2023.10476924

End-to-End Learning of Communication Systems with Novel Data-Efficient IIR Channel Identification

Abstract

Publikationsreihe

Wissenschaftszweige

JKU-Schwerpunkte

Zugriff auf Dokument

Andere Dateien und Links

Projekte

JKU LIT SAL eSPML Lab

Dieses zitieren