Polarized Dropout: A Novel Deep Joint Source Channel Coding Scheme for Erasure Channels
This letter investigates the challenges encountered by deep joint source-channel coding in erasure channels. We explore the effectiveness of the widely adopted dropout technique in endowing deep neural networks with resilience against erasures. However, directly applying dropout at the channel layer...
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| Veröffentlicht in: | IEEE communications letters Jg. 28; H. 9; S. 1986 - 1990 |
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| Abstract | This letter investigates the challenges encountered by deep joint source-channel coding in erasure channels. We explore the effectiveness of the widely adopted dropout technique in endowing deep neural networks with resilience against erasures. However, directly applying dropout at the channel layer introduces uncertainty into the neural network's training process, leading to performance degradation. To address this issue, we introduce the Polarized Dropout scheme and a novel network architecture that encodes analog symbols using the Walsh-Hadamard transform based on real-number field computation. Leveraging the polarization of symbol recovery probabilities, for a given erasure rate, a determined set of neurons will be assigned a dropout rate of 1, while the remainder are assigned a dropout rate of 0. Simulation results indicate a maximum enhancement of nearly 6dB in communication performance. |
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| AbstractList | This letter investigates the challenges encountered by deep joint source-channel coding in erasure channels. We explore the effectiveness of the widely adopted dropout technique in endowing deep neural networks with resilience against erasures. However, directly applying dropout at the channel layer introduces uncertainty into the neural network's training process, leading to performance degradation. To address this issue, we introduce the Polarized Dropout scheme and a novel network architecture that encodes analog symbols using the Walsh-Hadamard transform based on real-number field computation. Leveraging the polarization of symbol recovery probabilities, for a given erasure rate, a determined set of neurons will be assigned a dropout rate of 1, while the remainder are assigned a dropout rate of 0. Simulation results indicate a maximum enhancement of nearly 6dB in communication performance. |
| Author | Ren, Zichang Wang, Yiru Zhao, Yuping |
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| Cites_doi | 10.1109/TCCN.2019.2919300 10.1109/TCCN.2022.3151935 10.1109/TSP.2021.3071210 10.1109/GLOBECOM54140.2023.10436819 10.1109/TCCN.2017.2758370 10.1109/GLOBECOM54140.2023.10437020 10.1109/TCOMM.2023.3258487 10.1109/JSAC.2022.3223408 10.1109/JSAC.2023.3288252 10.1016/j.dsp.2021.103207 10.1109/TIT.2009.2021379 |
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| References | Hinton (ref11) 2012 ref13 ref10 ref2 ref1 ref8 ref7 ref9 ref4 ref3 ref6 ref5 Krizhevsky (ref14) 2009 Srivastava (ref12) 2014; 15 |
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| Snippet | This letter investigates the challenges encountered by deep joint source-channel coding in erasure channels. We explore the effectiveness of the widely adopted... |
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| SubjectTerms | Artificial neural networks Channels Coding Decoding deep learning erasure channel Joint source-channel coding Number theory Performance degradation polar code Receivers Symbols Training Transfer functions Transforms Vectors |
| Title | Polarized Dropout: A Novel Deep Joint Source Channel Coding Scheme for Erasure Channels |
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