TY - GEN
T1 - Neural Network Equalizers and Successive Interference Cancellation for Bandlimited Channels with a Nonlinearity
AU - Plabst, Daniel
AU - Prinz, Tobias
AU - Diedolo, Francesca
AU - Wiegart, Thomas
AU - Böcherer, Georg
AU - Hanik, Norbert
AU - Kramer, Gerhard
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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 information rates of joint detection and decoding (JDD) with considerably less complexity than JDD and other existing equalizers. Simulations for short-haul optical fiber links with square-law detection illustrate the gains.
AB - 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 information rates of joint detection and decoding (JDD) with considerably less complexity than JDD and other existing equalizers. Simulations for short-haul optical fiber links with square-law detection illustrate the gains.
UR - http://www.scopus.com/inward/record.url?scp=85189317494&partnerID=8YFLogxK
U2 - 10.1109/ISIT57864.2024.10619487
DO - 10.1109/ISIT57864.2024.10619487
M3 - Conference contribution
AN - SCOPUS:85189317494
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 1197
EP - 1202
BT - 2024 IEEE International Symposium on Information Theory, ISIT 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Symposium on Information Theory, ISIT 2024
Y2 - 7 July 2024 through 12 July 2024
ER -