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Improving Convergence of Iterative Decoders using Neural Networks| old_uid | 16028 |
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| title | Improving Convergence of Iterative Decoders using Neural Networks |
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| start_date | 2018/06/05 |
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| schedule | 11h-12h |
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| online | no |
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| location_info | Saint-Martin 2, espace des colloques |
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| summary | In this talk, we discuss the perspectives of utilizing deep neural networks (DNN) to decode Low-Density Parity Check (LDPC) codes. The main idea is to build a neural network to learn and optimize a conventional iterative decoder of LDPC codes. A DNN is based on Tanner graph, and the activation functions emulate message update functions in variable and check nodes. We impose a symmetry on weight matrices which makes it possible to train the DNN on a single codeword and noise realizations only. Based on the trained weights and the bias, we further quantize messages in such DNN-based decoder with 3-bit precision while maintaining no loss in error performance compared to the min-sum algorithm. We use examples to present that the DNN framework can be applied to various code lengths. The simulation results show that, the trained weights and bias make the iterative DNN decoder converge faster and thus achieve higher throughput at the cost of trivial additional decoding complexity. |
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| responsibles | Marinica |
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