arXiv:2605.28358v1 Announce Type: new Abstract: Error-correcting codes enable reliable communication, yet practical soft decoding remains challenging across code families and block lengths. We propose SB-ECC, a score-based decoder that casts decoding as continuous-time denoising. A neural denoiser defines a probability-flow ordinary differential equation (ODE) that iteratively updates the noisy channel observation toward a valid codeword, guided by parity constraints. The model is trained across noise levels without time/SNR conditioning, enabling inference without SNR estimation and supportin
The increasing demand for robust data transmission in AI, computing, and communications, coupled with advancements in neural network architectures, is driving innovation in error correction techniques.
Improved error correction decoding, especially without requiring SNR estimation, can significantly enhance the reliability and efficiency of digital communication and storage systems across critical infrastructure.
Decoding error-correcting codes may become more robust and less dependent on precise noise estimation, potentially enabling more resilient and efficient data transfer in various applications.
- · Telecommunications companies
- · Data center operators
- · AI hardware developers
- · Space exploration agencies
- · Legacy error-correction methods
- · Systems highly sensitive to noise estimation inaccuracies
More reliable data transmission across wireless, optical, and storage mediums.
Reduced need for redundant infrastructure or retransmissions, leading to efficiency gains and lower operational costs.
Enabling new classes of low-power or long-distance communication systems previously constrained by error rates, impacting edge computing and IoT.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG