LightCode: 有反馈信道的分析与神经编码
LightCode: Light Analytical and Neural Codes for Channels with Feedback
AI总结：
LightCode是一种轻量级神经编码方案，通过结合分析编码与深度学习技术，在低SNR区域实现高可靠性，同时降低计算和内存开销。
摘要：
有反馈信道中可靠且高效的编码设计仍然是通信理论中的长期挑战。尽管通过利用深度学习技术取得了显著进展，但神经编码往往面临计算成本高、缺乏可解释性以及在资源受限环境下实用性有限的问题。我们专注于设计低复杂度且可解释的编码方案。首先，我们证明了受Schalkwijk-Kailath (SK)和Gallager-Nakiboğlu (GN)方案启发的PowerBlast分析编码方案在SK和GN方案上实现了显著的可靠性提升，在高信噪比（SNR）区域优于神经编码。其次，为了在低SNR区域提高可靠性，我们提出LightCode，一种轻量级神经编码，其可靠性达到当前最佳水平，同时在内存和计算使用上仅为现有深度学习编码的分数。最后，我们系统分析了学习到的编码，建立了LightCode与PowerBlast之间的联系，识别了对性能至关重要的组件，并通过线性回归分析提供了解释。
英文摘要：
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-Nakiboğlu (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 deeplearning-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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
