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A Novel Sum-Product Detection Algorithm for Faster-Than-Nyquist Signaling: A Deep Learning Approach.
- Source :
- IEEE Transactions on Communications; Sep2021, Vol. 69 Issue 9, p5975-5987, 13p
- Publication Year :
- 2021
-
Abstract
- A deep learning assisted sum-product detection algorithm (DL-SPDA) for faster-than-Nyquist (FTN) signaling is proposed in this paper. The proposed detection algorithm works on a modified factor graph which concatenates a neural network function node to the variable nodes of the conventional FTN factor graph to approach the maximum a posterior probabilities (MAP) error performance. In specific, the neural network performs as a function node in the modified factor graph to deal with the residual intersymbol interference (ISI) that is not considered by the conventional detector with a limited complexity. We modify the updating rule in the conventional sum-product algorithm so that the neural network assisted detector can be complemented to a turbo equalization receiver. Furthermore, we propose a compatible training technique to improve the detection performance of the proposed DL-SPDA with turbo equalization. In particular, the neural network is optimized in terms of the mutual information between the transmitted sequence and the extrinsic information. We also investigate the maximum-likelihood bit error rate (BER) performance of a finite length coded FTN system. Simulation results show that the error performance of the proposed algorithm approaches the MAP performance, which is consistent with the analytical BER. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00906778
- Volume :
- 69
- Issue :
- 9
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Communications
- Publication Type :
- Academic Journal
- Accession number :
- 153710937
- Full Text :
- https://doi.org/10.1109/TCOMM.2021.3090026