LEARN Codes: Inventing Low-Latency Codes via Recurrent Neural Networks
LEARN Codes: Inventing Low-Latency Codes via Recurrent Neural Networks
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DOI：
10.1109/icc.2019.8761286
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发表时间：
2018-11
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影响因子：
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通讯作者：
Yihan Jiang;Hyeji Kim;Himanshu Asnani;Sreeram Kannan;Sewoong Oh;P. Viswanath
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文献类型：
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作者：
Yihan Jiang;Hyeji Kim;Himanshu Asnani;Sreeram Kannan;Sewoong Oh;P. Viswanath
在低延迟约束下设计信道编码是5G标准中最苛刻的要求之一。然而，传统代码的性能的清晰表征仅在大块长度限制下可用。代码设计以这些渐近分析为指导，需要大的块长度和长的延迟才能实现所需的错误率。此外，当为一个通道（例如加性高斯白噪声 (AWGN) 通道）设计的代码用于另一通道（例如非 AWGN 通道）时，需要启发式方法来实现任何重要的性能，从而严重缺乏鲁棒性和适应性。通过联合设计基于循环神经网络（RNN）的编码器和解码器，我们提出了一种端到端学习神经代码，其在块设置下优于规范卷积代码。凭借设计新型神经块代码的经验，我们提出了低延迟约束下的一类新代码——低延迟高效自适应鲁棒神经（LEARN）代码，其性能优于最先进的低延迟代码，并表现出鲁棒性和自适应特性。 LEARN 代码展示了通过现代深度学习工具与通信工程见解相结合，为未来通信设计新的多功能和通用代码的潜力。
Designing channel codes under low latency constraints is one of the most demanding requirements in 5G standards. However, sharp characterizations of the performances of traditional codes are only available in the large block lengths limit. Code designs are guided by those asymptotic analyses and require large block lengths and long latency to achieve the desired error rate. Furthermore, when the codes designed for one channel (e.g. Additive White Gaussian Noise (AWGN) channel) are used for another (e.g. non-AWGN channels), heuristics are necessary to achieve any non trivial performance — thereby severely lacking in robustness as well as adaptivity. Obtained by jointly designing recurrent neural network (RNN) based encoder and decoder, we propose an end-to-end learned neural code which outperforms canonical convolutional code under block settings. With this gained experience of designing a novel neural block code, we propose a new class of codes under low latency constraint — Low-latency Efficient Adaptive Robust Neural (LEARN) codes, which outperform the state-of-the-art low latency codes as well as exhibit robustness and adaptivity properties. LEARN codes show the potential of designing new versatile and universal codes for future communications via tools of modern deep learning coupled with communication engineering insights.