Low-Complexity Near-Optimum Symbol Detection Based on Neural Enhancement of Factor Graphs
| Autor: | Luca Schmid, Laurent Schmalen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Computer Science - Machine Learning Computer Science - Information Theory Information Theory (cs.IT) FOS: Electrical engineering electronic engineering information engineering Electrical Engineering and Systems Science - Signal Processing Electrical and Electronic Engineering ddc:620 Engineering & allied operations Machine Learning (cs.LG) |
| Zdroj: | IEEE Transactions on Communications, 70 (11), 7562–7575 |
| ISSN: | 0090-6778 1558-0857 |
| Popis: | We consider the application of the factor graph framework for symbol detection on linear inter-symbol interference channels. Based on the Ungerboeck observation model, a detection algorithm with appealing complexity properties can be derived. However, since the underlying factor graph contains cycles, the sum-product algorithm (SPA) yields a suboptimal algorithm. In this paper, we develop and evaluate efficient strategies to improve the performance of the factor graph-based symbol detection by means of neural enhancement. In particular, we consider neural belief propagation and generalizations of the factor nodes as an effective way to mitigate the effect of cycles within the factor graph. By applying a generic preprocessor to the channel output, we propose a simple technique to vary the underlying factor graph in every SPA iteration. Using this dynamic factor graph transition, we intend to preserve the extrinsic nature of the SPA messages which is otherwise impaired due to cycles. Simulation results show that the proposed methods can massively improve the detection performance, even approaching the maximum a posteriori performance for various transmission scenarios, while preserving a complexity which is linear in both the block length and the channel memory. Comment: revised version. arXiv admin note: text overlap with arXiv:2203.03333 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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