Abstract
The task of equalization on the receiver side of a wireless communication system is typically accomplished with model-based estimation methods. However, the utilization of data-driven approaches, e.g., neural networks (NNs), for equalization is in focus of current research. In this work, we investigate two different NNs for single-carrier frequency domain equalization. We elaborate on how existing model knowledge can be incorporated into NNs, we introduce a data normalization scheme required for the regarded NNs, and we compare these data-driven methods with model-based approaches concerning performance and complexity.
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Notes
- 1.
We refer to an NN whose structure is deduced from model knowledge as “model-inspired”.
- 2.
Let \(\textbf{v}_{\text {in}}^{(q)}\) and \(\textbf{v}_{\text {hid}}^{(q)}\) denote the input and the output of the qth hidden layer. When employing a weighted residual connection, the input \(\textbf{v}_{\text {in}}^{(q+1)}\) of the follow-up hidden layer is \(\textbf{v}_{\text {in}}^{(q+1)} = \beta \textbf{v}_{\text {in}}^{(q)} + (1-\beta )\textbf{v}_{\text {hid}}^{(q)}\), with \(\beta \in [0,1[\).
- 3.
The complexity analysis is conducted in the same way as in [1].
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Baumgartner, S., Lang, O., Huemer, M. (2022). Neural Network Based Single-Carrier Frequency Domain Equalization. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2022. EUROCAST 2022. Lecture Notes in Computer Science, vol 13789. Springer, Cham. https://doi.org/10.1007/978-3-031-25312-6_34
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