Deepcode and Modulo-SK are Designed for Different Settings

Simple Modulo can Significantly Outperform Deep Learning-based Deepcode

Deepcode (H.Kim et al.2018) is a recently suggested Deep Learning-based scheme for communication over the AWGN channel with noisy feedback, claimed to be superior to all previous schemes in the literature. Deepcode's use of nonlinear coding (via Deep Learning) has been inspired by known shortcomings (Y.-H. Kim et al 2007) of linear feedback schemes. In 2014, we presented a nonlinear feedback coding scheme based on a combination of the classical SK scheme and modulo-arithmetic, using a small number of elementary operations without any type of neural network. This Modulo-SK scheme has been omitt…

Deepcode: Feedback Codes via Deep Learning

The design of codes for communicating reliably over a statistically well defined channel is an important endeavor involving deep mathematical research and wide-ranging practical applications. In this work, we present the first family of codes obtained via deep learning, which significantly beats state-of-the-art codes designed over several decades of research. The communication channel under consideration is the Gaussian noise channel with feedback, whose study was initiated by Shannon; feedback is known theoretically to improve reliability of communication, but no practical codes that do so h…

在二进制模和问题中结构化编码改进随机编码的条件

The Condition for Structured Coding to Improve Random Coding in the Binary Modulo-sum Problem

本文分析刻画了多字母扩展AH编码优于Slepian-Wolf编码的条件，通过类型方法将多字母表达式简化为单字母散度的比较。