  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.
