Stefan Baumgartner, Oliver Lang, Mario Huemer,
"A Soft Interference Cancellation Inspired Neural Network for SC-FDE"
: Proceedings of the 2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC 2022), IEEE, 7-2022, ISBN: 978-1-6654-9455-7
Original Titel:
A Soft Interference Cancellation Inspired Neural Network for SC-FDE
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC 2022)
Original Kurzfassung:
Model-based estimation methods have been employed for the task of equalization since the beginning of digital communications. Due to the incredible success of data-driven machine learning approaches for many applications in different research disciplines, the replacement of model-based equalization methods by neural networks has been investigated recently. Incorporating model knowledge into a neural network is a possible approach for complexity reduction and performance enhancement, which is, however, very challenging. In this paper, we propose a novel neural network architecture for single carrier systems with frequency domain equalization inspired by a model-based soft interference cancellation scheme. We evaluate its bit error ratio performance in indoor frequency selective-environments and show that the proposed approach outperforms both model-based and data-driven state-of-the-art methods.