AWGN信道的非线性后验均值反馈编码

中文摘要 AI 辅助

反馈可以提高加性高斯白噪声（AWGN）信道上通信的可靠性。经典反馈码是可解释的，但往往依赖于线性估计，而深度学习的反馈码虽能实现强性能，却需要大量学习参数且难以解释。在这项工作中，我们提出了一种用于带反馈的AWGN信道的可解释后验均值反馈编码框架。所提出的方案利用后验均值细化，在无噪声被动反馈和有噪声主动反馈两种情况下构造非线性反馈码，并在接收端采用最大后验（MAP）解码。我们进一步开发了一种基于投影的设计，以提高在噪声反馈下的鲁棒性并支持更大的消息规模。数值结果表明，所提出的方案实现了强大的有限块长度性能，并优于若干分析和学习的反馈编码基线，同时仅使用少量学习设计参数。

英文摘要

Feedback can improve the reliability of communication over additive white Gaussian noise (AWGN) channels. Classical feedback codes are interpretable, or easy to understand, but often rely on linear estimation, while deep-learned feedback codes can achieve strong performance but require many learned parameters and are often black boxes that are difficult to interpret. In this work, we propose a new posterior-mean feedback coding framework for AWGN channels with feedback. The proposed scheme uses posterior-mean refinement to construct nonlinear feedback codes under both noiseless passive feedback and noisy active feedback, with maximum a posteriori (MAP) decoding at the receiver. This scheme is analytically described, but a few learned parameters related to power allocation, and hence we consider this an ``interpretable,'' or human-understandable code. We further develop a projection-based design to improve robustness under noisy feedback and support larger message sizes. Numerical results show that the proposed schemes achieve strong finite-blocklength performance and outperform several analytical and learned feedback coding baselines, while using only a small number of learned design parameters.

发表机构

• University of Illinois Chicago（伊利诺伊大学芝加哥分校）
• HUN-REN-SZTE Research Group on AI, University of Szeged（塞格德大学 HUN-REN-SZTE 人工智能研究组）

机构由 AI 辅助整理，请以论文原文为准。