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 selectiveenvironments and show that the proposed approach outperforms both model-based and data-driven state-of-the-art methods.
|Title of host publication||IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2022|
|Publication status||Accepted/In press - 2022|
- Model-inspired neural networks
- Single carrier frequency domain equalization
- Soft interference cancellation