Zobrazeno 1 - 10
of 81
pro vyhledávání: '"Boecherer, Georg"'
Autor:
Plabst, Daniel, Prinz, Tobias, Diedolo, Francesca, Wiegart, Thomas, Böcherer, Georg, Hanik, Norbert, Kramer, Gerhard
Neural networks (NNs) inspired by the forward-backward algorithm (FBA) are used as equalizers for bandlimited channels with a memoryless nonlinearity. The NN-equalizers are combined with successive interference cancellation (SIC) to approach the info
Externí odkaz:
http://arxiv.org/abs/2408.15767
Autor:
Plabst, Daniel, Prinz, Tobias, Diedolo, Francesca, Wiegart, Thomas, Böcherer, Georg, Hanik, Norbert, Kramer, Gerhard
Reliable communication over bandlimited and non-linear channels usually requires equalization to simplify receiver processing. Equalizers that perform joint detection and decoding (JDD) achieve the highest information rates but are often too complex
Externí odkaz:
http://arxiv.org/abs/2401.09217
Autor:
Arnold, Elias, Böcherer, Georg, Strasser, Florian, Müller, Eric, Spilger, Philipp, Billaudelle, Sebastian, Weis, Johannes, Schemmel, Johannes, Calabrò, Stefano, Kuschnerov, Maxim
Neuromorphic computing implementing spiking neural networks (SNN) is a promising technology for reducing the footprint of optical transceivers, as required by the fast-paced growth of data center traffic. In this work, an SNN nonlinear demapper is de
Externí odkaz:
http://arxiv.org/abs/2302.14726
Autor:
Lentner, Diego, Yacoub, Emna Ben, Calabrò, Stefano, Böcherer, Georg, Stojanović, Nebojša, Kramer, Gerhard
Concatenated forward error correction is studied using an outer KP4 Reed-Solomon code with hard-decision decoding and inner single parity check (SPC) codes with Chase/Wagner soft-decision decoding. Analytical expressions are derived for the end-to-en
Externí odkaz:
http://arxiv.org/abs/2212.10523
The last decade has witnessed the breakthrough of deep neural networks (DNNs) in many fields. With the increasing depth of DNNs, hundreds of millions of multiply-and-accumulate (MAC) operations need to be executed. To accelerate such operations effic
Externí odkaz:
http://arxiv.org/abs/2211.14917
Autor:
Hossain, Md Sabbir-Bin, Böcherer, Georg, Lin, Youxi, Li, Shuangxu, Calabrò, Stefano, Nedelcu, Andrei, Rahman, Talha, Wettlin, Tom, Wei, Jinlong, Stojanović, Nebojša, Xie, Changsong, Kuschnerov, Maxim, Pachnicke, Stephan
For 200Gb/s net rates, cap probabilistic shaped PAM-8 with different Gaussian orders are experimentally compared against uniform PAM-8. In back-to-back and 5km measurements, cap-shaped 85-GBd PAM-8 with Gaussian order of 5 outperforms 71-GBd uniform
Externí odkaz:
http://arxiv.org/abs/2206.07142
An entropy-regularized mean square error (MSE-X) cost function is proposed for nonlinear equalization of short-reach optical channels. For a coherent optical transmission experiment, MSE-X achieves the same bit error rate as the standard MSE cost fun
Externí odkaz:
http://arxiv.org/abs/2206.01004
Autor:
Arnold, Elias, Böcherer, Georg, Müller, Eric, Spilger, Philipp, Schemmel, Johannes, Calabrò, Stefano, Kuschnerov, Maxim
A spiking neural network (SNN) non-linear equalizer model is implemented on the mixed-signal neuromorphic hardware system BrainScaleS-2 and evaluated for an IM/DD link. The BER 2e-3 is achieved with a hardware penalty less than 1 dB, outperforming nu
Externí odkaz:
http://arxiv.org/abs/2206.00401
Autor:
Hossain, Md Sabbir-Bin, Boecherer, Georg, Rahman, Talha, Stojanovic, Nebojsa, Schulte, Patrick, Calabrò, Stefano, Wei, Jinlong, Bluemm, Christian, Wettlin, Tom, Xie, Changsong, Kuschnerov, Maxim, Pachnicke, Stephan
Publikováno v:
2021 European Conference on Optical Communication (ECOC)
For 200Gbit/s net rates, uniform PAM-4, 6 and 8 are experimentally compared against probabilistic shaped PAM-8 cap and cup variants. In back-to-back and 20km measurements, cap shaped 80GBd PAM-8 outperforms 72GBd PAM-8 and 83GBd PAM-6 by up to 3.50dB
Externí odkaz:
http://arxiv.org/abs/2205.08805
Autor:
Arnold, Elias, Böcherer, Georg, Müller, Eric, Spilger, Philipp, Schemmel, Johannes, Calabrò, Stefano, Kuschnerov, Maxim
A spiking neural network (SNN) equalizer model suitable for electronic neuromorphic hardware is designed for an IM/DD link. The SNN achieves the same bit-error-rate as an artificial neural network, outperforming linear equalization.
Externí odkaz:
http://arxiv.org/abs/2205.04263