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：带反馈信道的可靠高效编码设计仍是通信理论中长期存在的挑战。尽管深度学习技术带来了显著改进，但神经编码常面临高计算成本、缺乏可解释性以及在资源受限场景中实用性有限的困境。本文聚焦于设计低复杂度、可解释且更适用于通信系统的编码方案，同时推进分析型与神经编码两方面研究。首先，我们证明受Schalkwijk-Kailath（SK）与Gallager-Nakiboglu（GN）方案启发的分析型编码POWERBLAST，在可靠性上取得优于SK和GN方案的显著提升，并在高信噪比（SNR）区域超越神经编码性能。其次，为提升低SNR区域的可靠性，提出轻量级神经编码LIGHTCODE，该方案在保持最先进可靠性的同时，仅消耗现有深度学习编码方案极小部分的内存与计算资源。最后，我们系统分析所学编码，建立LIGHTCODE与POWERBLAST之间的关联，识别对性能至关重要的组件，并通过线性回归分析辅助实现可解释性。