All You Need Is Feedback: Communication With Block Attention Feedback Codes
All You Need Is Feedback: Communication With Block Attention Feedback Codes
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DOI：
10.1109/jsait.2022.3223901
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
Emre Ozfatura;Yulin Shao;A. Perotti;B. Popović;Deniz Gündüz
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作者：
Emre Ozfatura;Yulin Shao;A. Perotti;B. Popović;Deniz Gündüz
基于深度神经网络(DNN)的信道码设计作为传统编码方案的替代方案最近引起了人们的兴趣，尤其是对于其中现有编码不能提供令人满意的性能的信道。存在反馈的编码就是这样一个问题，最近各种基于DNN的编码体系结构已经在这个问题上取得了可喜的结果。本文介绍了一种新的学习辅助反馈码设计，称为广义块注意反馈(GBAF)码，与现有的解决方案相比，它在误块率(BLER)方面获得了数量级的改善。序列到序列编码和消息比特的逐块处理是GBAF码的两个重要设计原则，由于发送器和接收器之间的交互较少，因此不仅减少了通信开销，而且允许灵活的编码率。GBAF码还具有模块化结构，可以使用不同的神经网络结构来实现。在这项工作中，我们采用了流行的转换器结构，在反馈信道无噪声的情况下，在低信噪比条件下，该结构在BLER方面优于所有基于DNN的代码设计。
Deep neural network (DNN)-based channel code designs have recently gained interest as an alternative to conventional coding schemes, particularly for channels in which existing codes do not provide satisfactory performance. Coding in the presence of feedback is one such problem, for which promising results have recently been obtained by various DNN-based coding architectures. In this paper, we introduce a novel learning-aided feedback code design, dubbed generalized block attention feedback (GBAF) codes, that achieves orders-of-magnitude improvements in block error rate (BLER) compared to existing solutions. Sequence-to-sequence encoding and block-by-block processing of the message bits are the two important design principles of the GBAF codes, which not only reduce the communication overhead, due to fewer interactions between the transmitter and receiver, but also enable flexible coding rates. GBAF codes also have a modular structure that can be implemented using different neural network architectures. In this work, we employ the popular transformer architecture, which outperforms all the prior DNN-based code designs in terms in terms of BLER BLER in the low signal-to-noise ratio regime when the feedback channel is noiseless.