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PDD-Based Decoder for LDPC Codes With Model-Driven Neural Networks
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DOI：10.1109/LCOMM.2022.3199747.png)
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Abstract
En 中文
In this work, we develop a double-loop iterative decoding algorithm for low density parity check (LDPC) codes based on the penalty dual decomposition (PDD) framework. We utilize the linear programming (LP) relaxation and the penalty method to handle the discrete constraints and the over-relaxation method is employed to improve convergence. Then, we unfold the proposed PDD decoding algorithm into a model-driven neural network, namely the learnable PDD decoding network (LPDN). We turn the tunable coefficients and parameters in the proposed PDD decoder into layer-dependent trainable parameters which can be optimized by gradient descent-based methods during network training. Simulation results demonstrate that the proposed LPDN with well-trained parameters is able to provide superior error-correction performance with much lower computational complexity as compared to the PDD decoder.
Keywords:
Decoding
Iterative decoding
Signal processing algorithms
Neural networks
Computational complexity
Convex functions
Convergence
LDPC codes
penalty dual decomposition
deep learning
deep unfolding
model-driven