Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels
Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels
复制标题
DOI：
--
复制
发表时间：
2019-11
期刊：
影响因子：
--
通讯作者：
Yihan Jiang;Hyeji Kim;Himanshu Asnani;Sreeram Kannan;Sewoong Oh;P. Viswanath
中科院分区：
文献类型：
--
作者：
Yihan Jiang;Hyeji Kim;Himanshu Asnani;Sreeram Kannan;Sewoong Oh;P. Viswanath
在通信媒介中打击噪声的代码仍然是信息理论和无线通信的重要研究领域。经过60多年的研究，数学家已经开发了渐近最佳的通道代码，用于在规范模型下进行交流。另一方面，在许多非典型通道设置中，最佳代码不存在，并且设计用于规范模型的代码是通过启发式方法适应这些通道的，因此不能保证是最佳的。在这项工作中，我们通过设计一个完全端到端的联合培训的神经编码器和解码器，即涡轮自动编码器（Turboae），并具有以下贡献：（a）在中等块长度下，涡轮增压器接近涡轮增压器，我们在这项问题上取得了重大进展。规范渠道下的最先进的表现； （b）此外，就可靠性而言，涡轮增压器的表现优于非规范环境下的最新代码。 Turboae表明，通道编码设计的开发可以通过深度学习来自动化，并且具有近乎最佳的性能。
Designing codes that combat the noise in a communication medium has remained a significant area of research in information theory as well as wireless communications. Asymptotically optimal channel codes have been developed by mathematicians for communicating under canonical models after over 60 years of research. On the other hand, in many non-canonical channel settings, optimal codes do not exist and the codes designed for canonical models are adapted via heuristics to these channels and are thus not guaranteed to be optimal. In this work, we make significant progress on this problem by designing a fully end-to-end jointly trained neural encoder and decoder, namely, Turbo Autoencoder (TurboAE), with the following contributions: (a) under moderate block lengths, TurboAE approaches state-of-the-art performance under canonical channels; (b) moreover, TurboAE outperforms the state-of-the-art codes under non-canonical settings in terms of reliability. TurboAE shows that the development of channel coding design can be automated via deep learning, with near-optimal performance.