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.

This paper introduces a novel approach called "friendly attack" aimed at enhancing the performance of error correction channel codes. Inspired by the concept of adversarial attacks, our method leverages the idea of introducing slight perturbations to the neural network input, resulting in a substantial impact on the network's performance. By introducing small perturbations to fixed-point modulated codewords before transmission, we effectively improve the decoder's performance without violating the input power constraint. The perturbation design is accomplished by a modified iterative fast gradient method. This study investigates various decoder architectures suitable for computing gradients to obtain the desired perturbations. Specifically, we consider belief propagation (BP) for LDPC codes; the error correcting code transformer, BP and neural BP (NBP) for polar codes, and neural BCJR for convolutional codes. We demonstrate that the proposed friendly attack method can improve the reliability across different channels, modulations, codes, and decoders. This method allows us to increase the reliability of communication with a legacy receiver by simply modifying the transmitted codeword appropriately.