KO codes: inventing nonlinear encoding and decoding for reliable wireless communication via deep-learning
KO codes: inventing nonlinear encoding and decoding for reliable wireless communication via deep-learning
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发表时间：
2021-08
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通讯作者：
Ashok Vardhan Makkuva;Xiyang Liu;Mohammad Vahid Jamali;Hessam Mahdavifar;Sewoong Oh;P. Viswanath
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作者：
Ashok Vardhan Makkuva;Xiyang Liu;Mohammad Vahid Jamali;Hessam Mahdavifar;Sewoong Oh;P. Viswanath
里程碑代码支持可靠的物理层通信，例如Reed-Muller， BCH, Convolution, Turbo， LDPC和Polar代码：每个代码都是线性代码，代表了数学上的突破。对人类的影响是巨大的：这些代码中的每一个都被用于全球无线通信标准（卫星、WiFi、蜂窝）。在经典加性高斯白噪声（AWGN）信道上的通信可靠性使不同代码的基准测试和排名成为可能。在本文中，我们构建了KO编码，这是一种计算效率高的深度学习驱动（编码器，解码器）对，在标准化AWGN信道上优于最先进的可靠性性能。在AWGN信道上具有挑战性的中短块长度制度下，KO码在低复杂度的连续对消解码下击败了最先进的Reed-Muller和Polar码。我们表明，KO码的增益主要是由于信息位的非线性映射直接传输真实符号（绕过调制），但拥有一个高效，高性能的解码器。实现这一目标的关键技术创新是一种新型神经架构的设计，其灵感来自于Reed-Muller和Polar代码中心的{\bfKronecker}操作{\bf（KO）的计算树。这些架构为发现迄今为止尚未探索的更丰富的非线性代数结构铺平了道路。代码可在 }\href{https://github.com/deepcomm/KOcodes}{https://github.com/deepcomm/KOcodes}
Landmark codes underpin reliable physical layer communication, e.g., Reed-Muller, BCH, Convolution, Turbo, LDPC and Polar codes: each is a linear code and represents a mathematical breakthrough. The impact on humanity is huge: each of these codes has been used in global wireless communication standards (satellite, WiFi, cellular). Reliability of communication over the classical additive white Gaussian noise (AWGN) channel enables benchmarking and ranking of the different codes. In this paper, we construct KO codes, a computationaly efficient family of deep-learning driven (encoder, decoder) pairs that outperform the state-of-the-art reliability performance on the standardized AWGN channel. KO codes beat state-of-the-art Reed-Muller and Polar codes, under the low-complexity successive cancellation decoding, in the challenging short-to-medium block length regime on the AWGN channel. We show that the gains of KO codes are primarily due to the nonlinear mapping of information bits directly to transmit real symbols (bypassing modulation) and yet possess an efficient, high performance decoder. The key technical innovation that renders this possible is design of a novel family of neural architectures inspired by the computation tree of the {\bf K}ronecker {\bf O}peration (KO) central to Reed-Muller and Polar codes. These architectures pave way for the discovery of a much richer class of hitherto unexplored nonlinear algebraic structures. The code is available at \href{https://github.com/deepcomm/KOcodes}{https://github.com/deepcomm/KOcodes}