Work

Concatenated Classic and Neural (CCN) Codes: ConcatenatedAE

Year: 2023

Type: conference-paper

Abstract: Small neural networks (NNs) used for error correction were shown to improve on classic channel codes and to address channel model changes. We extend the code dimension of any such structure by using the same NN under one-hot encoding multiple times, then serially-concatenated with an outer classic code. We design NNs with the same network parameters, where each Reed-Solomon codeword symbol is an input to a different NN. Significant improvements in block error probabilities for an additive Gaussian noise channel as compared to the small neural code are illustrated, as well as robustness to channel model changes.

Source: IEEE Wireless Communications and Networking Conference

DOI: 10.1109/wcnc55385.2023.10118867

Authors: Onur Günlü, Rick Fritschek, Rafael F. Schaefer

Institutions: Linköping University, Technische Universität Dresden

Language: English

Topic: Error Correcting Code Techniques

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Ministry of Education, Bundesministerium für Bildung und Forschung

Awards: Verbundprojekt: Code-basierte Physical-Layer Security für Terahertz-MIMO-Kommunikation - CERTAIN -, Teilvorhaben: Informationstheoretische Analyse sicherer Terahertz-MIMO-Kommunikation, Verbundprojekt: Post Shannon Kommunikation - NewCom -, Teilvorhaben: Physikalische Dienste-Integrations-Konzepte für neue Kommunikationsmodelle