Abstract
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.
Original language | English |
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Article number | 2717 |
Pages (from-to) | 1-15 |
Journal | Electronics (Switzerland) |
Volume | 11 |
Issue number | 17 |
DOIs | |
Publication status | Published - Sept 2022 |
Keywords
- deep learning
- iterative decoding
- quadratic residue codes
ASJC Scopus subject areas
- Control and Systems Engineering
- Signal Processing
- Hardware and Architecture
- Computer Networks and Communications
- Electrical and Electronic Engineering