TU3.R1.4

Graph Neural Network-based Joint Equalization and Decoding

Jannis Clausius, Marvin Geiselhart, Daniel Tandler, Stephan ten Brink, University of Stuttgart, Germany
Session:
Deep Learning in Communications
Track:
8: Deep Learning (such as understanding large language models)
Location:
Ballroom II & III
Presentation Time:
Tue, 9 Jul, 15:25 - 15:45
Session Chair:
Iñaki Esnaola,
Presentation
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Discussion
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Session TU3.R1
TU3.R1.1: PAC Learnability for Reliable Communication over Discrete Memoryless Channels
Jiakun Liu, Wenyi Zhang, University of Science and Technology of China, China; H. Vincent Poor, Princeton University, United States
TU3.R1.2: Neural Estimation of Multi-User Capacity Regions over Discrete Channels
Bashar Huleihel, Dor Tsur, Ziv Aharoni, Ben Gurion University of the Negev, Israel; Oron Sabag, The Hebrew University of Jerusalem, Israel; Haim Permuter, Ben Gurion University of the Negev, Israel
TU3.R1.3: Neural Network Equalizers and Successive Interference Cancellation for Bandlimited Channels with a Nonlinearity
Daniel Plabst, Tobias Prinz, Francesca Diedolo, Thomas Wiegart, Technical University of Munich, Germany; Georg Böcherer, Huawei Technologies Düsseldorf GmbH, Germany; Norbert Hanik, Gerhard Kramer, Technical University of Munich, Germany
TU3.R1.4: Graph Neural Network-based Joint Equalization and Decoding
Jannis Clausius, Marvin Geiselhart, Daniel Tandler, Stephan ten Brink, University of Stuttgart, Germany
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