Physical Layer Communication via Deep Learning
Physical Layer Communication via Deep Learning
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DOI：
10.1109/jsait.2020.2991562
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
Hyeji Kim;Sewoong Oh;P. Viswanath
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文献类型：
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作者：
Hyeji Kim;Sewoong Oh;P. Viswanath
可靠的数字通信是现代信息时代的主要主力。通信、编码和信息理论的学科通过设计有效的代码来推动创新，这些代码允许传输被健壮而高效地解码。几十年来，近乎最优码的进步是由人类个人的聪明才智取得的，而突破一直是零星的，并在几十年内传播开来。深度学习是日常生活的一部分，它的成功可以归因于缺乏(数学)生成模型。深度学习将神经网络模型与数据进行了经验匹配，结果非常有效。在其他应用中，数据是由数学上精确的简单模型和性能标准生成的，并且训练/测试样本无限丰富，但是算法选择的空间是巨大的(例如：国际象棋)。深度学习最近在这些问题上也显示出了很强的前景(例如：字母零)。后一种情景很好地描述了传播理论。规范通信信道下的数学模型允许对无限数量的数据进行采样以训练和测试通信算法((编码器、解码器)对)，并且比特(或块)错误率的度量允许数学上精确的评估。由于深度学习在国际象棋、围棋和蛋白质折叠等数学上定义明确且极具挑战性的任务中取得的成功，我们假设深度学习方法可以在解决编码理论的核心目标方面发挥关键作用：设计新的(编码器、解码器)对，改善标准信道模型的最新性能。这份手稿概述了证明这一假设的最新进展，重点是通过深度学习方法加强特定的编码方法家族-序列码(卷积码)和将其用作基本构建块的代码(Turbo码)。在几个规范信道上得出了新的技术水平结果，包括具有反馈的AWGN信道。
Reliable digital communication is a primary workhorse of the modern information age. The disciplines of communication, coding, and information theories drive the innovation by designing efficient codes that allow transmissions to be robustly and efficiently decoded. Progress in near optimal codes is made by individual human ingenuity over the decades, and breakthroughs have been, befittingly, sporadic and spread over several decades. Deep learning is a part of daily life where its successes can be attributed to a lack of a (mathematical) generative model. Deep learning empirically fits neural network models to the data, and the result has been extremely potent. In yet other applications, the data is generated by a simple model and performance criterion mathematically precise and training/test samples infinitely abundant, but the space of algorithmic choices is enormous (example: chess). Deep learning has recently shown strong promise in these problems too (example: alphazero). The latter scenario is a good description of communication theory. The mathematical models underlying canonical communication channels allow one to sample an unlimited amount of data to train and test the communication algorithms ((encoder, decoder) pairs) and the metric of bit (or block) error rate allows for mathematically precise evaluation. Motivated by the successes of deep learning in mathematically well defined and extremely challenging tasks of chess, Go, and protein folding, we posit that deep learning methods can play a crucial role in solving core goals of coding theory: designing new (encoder, decoder) pairs that improve state of the art performance over canonical channel models. This manuscript surveys recent advances towards demonstrating this hypothesis, by focusing on strengthening a specific family of coding methods – sequential codes (convolutional codes) and codes that use them as basic building blocks (Turbo codes) – via deep learning methods. New state of the art results are derived on several canonical channels, including the AWGN channel with feedback.