| Autor: |
Ming Wang, Yong Li, Rui Liu, Huihui Wu, Youqiang Hu, Francis C. M. Lau |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Electronics; Volume 11; Issue 17; Pages: 2717 |
| ISSN: |
2079-9292 |
| DOI: |
10.3390/electronics11172717 |
| Popis: |
In this paper, a low-complexity decoder based on a neural network is proposed to decode binary quadratic residue (QR) codes. The proposed decoder is based on the neural min-sum algorithm and the modified random redundant decoder (mRRD) algorithm. This new method has the same asymptotic time complexity as the min-sum algorithm, which is much lower than the difference on syndromes (DS) algorithm. Simulation results show that the proposed algorithm achieves a gain of more than 0.4 dB when compared to the DS algorithm. Furthermore, a simplified approach based on trapping sets is applied to reduce the complexity of the mRRD. This simplification leads to a slight loss in error performance and a reduction in implementation complexity. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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