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pro vyhledávání: '"A, Geiselhart"'
For asynchronous transmission of short blocks, preambles for packet detection contribute a non-negligible overhead. To reduce the required preamble length, joint detection and decoding (JDD) techniques have been proposed that additionally utilize the
Externí odkaz:
http://arxiv.org/abs/2409.01119
This paper proposes to use graph neural networks (GNNs) for equalization, that can also be used to perform joint equalization and decoding (JED). For equalization, the GNN is build upon the factor graph representations of the channel, while for JED,
Externí odkaz:
http://arxiv.org/abs/2401.16187
Autor:
Zunker, Andreas, Geiselhart, Marvin, Johannsen, Lucas, Kestel, Claus, Brink, Stephan ten, Vogt, Timo, Wehn, Norbert
Row-merged polar codes are a family of pre-transformed polar codes (PTPCs) with little precoding overhead. Providing an improved distance spectrum over plain polar codes, they are capable to perform close to the finite-length capacity bounds. However
Externí odkaz:
http://arxiv.org/abs/2312.14749
Pre-transformed polar codes (PTPCs) form a class of codes that perform close to the finite-length capacity bounds. The minimum distance and the number of minimum weight codewords are two decisive properties for their performance. In this work, we pro
Externí odkaz:
http://arxiv.org/abs/2311.17774
Autor:
Johannsen, Lucas, Kestel, Claus, Geiselhart, Marvin, Vogt, Timo, Brink, Stephan ten, Wehn, Norbert
The discovery of suitable automorphisms of polar codes gained a lot of attention by applying them in Automorphism Ensemble Decoding (AED) to improve the error-correction performance, especially for short block lengths. This paper introduces Successiv
Externí odkaz:
http://arxiv.org/abs/2306.16245
Isolated training with Gaussian priors (TGP) of the component autoencoders of turbo-autoencoder architectures enables faster, more consistent training and better generalization to arbitrary decoding iterations than training based on deep unfolding. W
Externí odkaz:
http://arxiv.org/abs/2305.09216
For short-packet, low-latency communications over random access channels, piloting overhead significantly reduces spectral efficiency. Therefore, pilotless systems recently gained attraction. While blind phase estimation algorithms such as Viterbi-Vi
Externí odkaz:
http://arxiv.org/abs/2305.01972
Recently, automorphism ensemble decoding (AED) has drawn research interest as a more computationally efficient alternative to successive cancellation list (SCL) decoding of polar codes. Although AED has demonstrated superior performance for specific
Externí odkaz:
http://arxiv.org/abs/2305.01214
The URLLC scenario in the upcoming 6G standard requires low latency and ultra reliable transmission, i.e., error correction towards ML performance. Achieving near-ML performance is very challenging especially for short block lengths. Polar codes are
Externí odkaz:
http://arxiv.org/abs/2303.01235
For improving short-length codes, we demonstrate that classic decoders can also be used with real-valued, neural encoders, i.e., deep-learning based codeword sequence generators. Here, the classical decoder can be a valuable tool to gain insights int
Externí odkaz:
http://arxiv.org/abs/2212.10355