The design of reliable and efficient codes for channels with feedback remains a longstanding challenge in communication theory. While significant improvements have been achieved by leveraging deep learning techniques, neural codes often suffer from high computational costs, a lack of interpretability, and limited practicality in resource-constrained settings. We focus on designing low-complexity coding schemes that are interpretable and more suitable for communication systems. We advance both analytical and neural codes. First, we demonstrate that POWERBLAST, an analytical coding scheme inspired by Schalkwijk-Kailath (SK) and Gallager-Nakiboglu (GN) schemes, achieves notable reliability improvements over both SK and GN schemes, outperforming neural codes in high signal-to-noise ratio (SNR) regions. Next, to enhance reliability in low-SNR regions, we propose LIGHTCODE, a lightweight neural code that achieves state-of-the-art reliability while using a fraction of memory and compute compared to existing deep-learning-based codes. Finally, we systematically analyze the learned codes, establishing connections between LIGHTCODE and POWERBLAST, identifying components crucial for performance, and providing interpretation aided by linear regression analysis.

翻译：在通信理论中，面向带反馈信道的可靠高效编码设计仍是一项长期挑战。尽管利用深度学习技术已取得显著改进，但神经编码常面临高计算成本、缺乏可解释性以及资源受限场景下实用性有限等问题。本文聚焦设计低复杂度且可解释的编码方案，使之更适用于通信系统。我们同时推进了分析与神经编码两大方向：首先，证明受Schalkwijk-Kailath（SK）方案和Gallager-Nakiboglu（GN）方案启发的分析型编码方案POWERBLAST，相较于SK与GN方案在可靠性上取得显著提升，并在高信噪比（SNR）区域超越神经编码；其次，为提升低SNR区域的可靠性，我们提出轻量神经编码LIGHTCODE，该方案以相较现有深度学习编码方案数倍量级更低的存储与计算开销实现当前最优可靠性；最后，我们系统分析了学习所得编码的参数，建立LIGHTCODE与POWERBLAST之间的内在联系，识别关键性能组件，并通过线性回归分析为编码机理提供辅助性解释。