论文标题
注意码：短包通信的超可靠反馈代码
AttentionCode: Ultra-Reliable Feedback Codes for Short-Packet Communications
论文作者
论文摘要
超可靠的短包通信是具有关键应用程序的未来无线网络的主要挑战。为了实现超过99.999％的超高通信，本文设想了一种基于相互作用的新通信范式，从而利用了接收器的反馈。我们提出了注意码，这是一类新的反馈代码，利用深度学习（DL）技术。注意码的基础是三个建筑创新：注意网络，投入重组和适应褪色渠道，并伴随着几种培训方法，包括大批量培训，分布式学习，预测优化器，训练测试测试的信号 - 噪声 - 噪声比率（SNR）的比例（SNR）次数（SNR）MISCATCH和课程学习。通过机器学习，培训方法可能会推广到其他无线通信应用程序。数值实验验证了注意码在加性白色高斯噪声（AWGN）通道和褪色通道中的所有基于DL的反馈代码之间建立了新的最新技术状态。例如，在带有无噪声反馈的AWGN通道中，注意码达到了$ 10^{ - 7} $ $ 10^{ - 7} $的块错误率（BLER），当远期通道SNR为0 dB，块大小为50位，这表明了注意码提供超可靠的短包通信的潜力。
Ultra-reliable short-packet communication is a major challenge in future wireless networks with critical applications. To achieve ultra-reliable communications beyond 99.999%, this paper envisions a new interaction-based communication paradigm that exploits feedback from the receiver. We present AttentionCode, a new class of feedback codes leveraging deep learning (DL) technologies. The underpinnings of AttentionCode are three architectural innovations: AttentionNet, input restructuring, and adaptation to fading channels, accompanied by several training methods, including large-batch training, distributed learning, look-ahead optimizer, training-test signal-to-noise ratio (SNR) mismatch, and curriculum learning. The training methods can potentially be generalized to other wireless communication applications with machine learning. Numerical experiments verify that AttentionCode establishes a new state of the art among all DL-based feedback codes in both additive white Gaussian noise (AWGN) channels and fading channels. In AWGN channels with noiseless feedback, for example, AttentionCode achieves a block error rate (BLER) of $10^{-7}$ when the forward channel SNR is 0 dB for a block size of 50 bits, demonstrating the potential of AttentionCode to provide ultra-reliable short-packet communications.
