Deep learning based channel code designs have recently gained interest as an alternative to conventional coding algorithms, particularly for channels for which existing codes do not provide effective solutions. Communication over a feedback channel is one such problem, for which promising results have recently been obtained by employing various deep learning architectures. In this paper, we introduce a novel learning-aided code design for feedback channels, called generalized block attention feedback (GBAF) codes, which i) employs a modular architecture that can be implemented using different neural network architectures; ii) provides order-of-magnitude improvements in the probability of error compared to existing designs; and iii) can transmit at desired code rates.
翻译:作为常规编码算法的替代方法,特别是对于现有编码无法提供有效解决办法的频道,深层学习频道代码设计最近引起了人们的兴趣,因为现有编码无法提供有效解决办法的频道,通过反馈频道进行沟通就是其中的一个问题,最近通过使用各种深层学习结构,取得了有希望的成果。 在本文中,我们为反馈渠道引入了一种新的学习辅助代码设计,称为 " 普遍区块关注反馈(GBAF)代码 ",其中i)使用了模块化结构,可使用不同的神经网络结构实施;ii)提供了与现有设计相比,误差概率的测高;以及iii)能够以预期的代码速率传输。