2025/06/11 by Shubham Srivastava, Srivastava, Shubham, Adrish Banerjee +1
Computer Science · Engineering · #Advanced Wireless Communication Techniques #Error Correcting Code Techniques #FOS: Computer and information sciences #Information Theory (cs.IT) #Wireless Signal Modulation Classification
paper · pdf · doi:10.48550/arxiv.2506.10121
openalex publication_date 2025/06/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper introduces HiKO (Hierarchical Kronecker Operation), a novel framework for training high-rate neural error-correcting codes that enables KO codes to outperform Reed-Muller codes beyond second order. To our knowledge, this is the first attempt to extend KO codes beyond second order. While conventional KO codes show promising results for low-rate regimes (r < 2), they degrade at higher rates -- a critical limitation for practical deployment. Our framework incorporates three key innovations: (1) a hierarchical training methodology that decomposes complex high-rate codes into simpler constituent codes for efficient knowledge transfer, (2) enhanced neural architectures with dropout regularization and learnable skip connections tailored for the Plotkin structure, and (3) a progressive unfreezing strategy that systematically transitions from pre-trained components to fully optimized integrated codes. Our experiments show that HiKO codes consistently outperform traditional Reed-Muller codes across various configurations, achieving notable performance improvements for third-order (r = 3) and fourth-order (r = 4) codes. Analysis reveals that HiKO codes successfully approximate Shannon-optimal Gaussian codebooks while preserving efficient decoding properties. This represents the first successful extension of KO codes beyond second order, opening new possibilities for neural code deployment in high-throughput communication systems.