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.

翻译：本文提出了一种名为“友好攻击”的新方法，旨在提升纠错信道码的性能。受对抗攻击概念的启发，该方法利用对神经网络输入施加微小扰动从而对网络性能产生显著影响的思路。通过在传输前对定点调制的码字引入微小扰动，我们在不违反输入功率约束的条件下有效提升了解码器的性能。扰动设计通过改进的迭代快速梯度法实现。本研究探索了适用于计算梯度以获取所需扰动的多种解码器架构。具体而言，我们考虑了LDPC码的置信传播（BP）算法；极化码的纠错码变换器、BP及神经BP（NBP）算法；以及卷积码的神经BCJR算法。实验证明，所提出的友好攻击方法能够提升不同信道、调制方式、编码及解码器下的可靠性。该方法仅需适当修改传输码字，即可提升传统接收机的通信可靠性。