  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.
