.png){kind=icon}
.png){kind=icon}
Return
Sparse graph neural network aided efficient decoder for polar codes under bursty interference
.png){kind=icon}
OA
AI
DOI：10.1016/j.dcan.2023.12.002.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, a sparse graph neural network-aided (SGNN-aided) decoder is proposed for improving the decoding performance of polar codes under bursty interference. Firstly, a sparse factor graph is constructed using the encoding characteristic to achieve high-throughput polar decoding. To further improve the decoding performance, a residual gated bipartite graph neural network is designed for updating embedding vectors of heterogeneous nodes based on a bidirectional message passing neural network. This framework exploits gated recurrent units and residual blocks to address the gradient disappearance in deep graph recurrent neural networks. Finally, predictions are generated by feeding the embedding vectors into a readout module. Simulation results show that the proposed decoder is more robust than the existing ones in the presence of bursty interference and exhibits high universality.
Keywords:
Sparse graph neural network
Polar codes
Bursty interference
Sparse factor graph
Message passing neural network
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.5
Papers:
406
Citations:
3.5K