TransCoder: a transformer-based neural-enhancement framework for channel codes

Official URL: https://dx.doi.org/10.1109/TCOMM.2026.3717020

Communication over noisy channels relies on error-correcting codes (ECCs) tailored to system constraints. Neural decoders can improve ECC reliability, yet their high computational complexity hinders practical deployment. We instead design a transformer-based transmission scheme that improves the reliability of existing ECCs without replacing them. We call this approach TransCoder, alluding both to its function and architecture. TransCoder operates as a code-adaptive module deployable at the transmitter, the receiver, or both. A block-attention neural decoder iteratively refines the channel observations together with the soft outputs of a conventional decoder. Across LDPC, BCH, Polar, and Turbo codes and a wide SNR range, TransCoder significantly lowers the block error rate (BLER) at complexity comparable to conventional decoders. Gains are largest at moderate blocklengths (64-512) and lower rates, regimes in which existing neural decoders struggle despite their much higher complexity. For 5G NR LDPC codes with blocklengths ≥400, the decoder-only variant still achieves significant performance improvements, especially at high SNR. These results position TransCoder as a practical solution for resource-constrained wireless devices.