Feedback Code

Feedback codes leverage receiver information to improve communication reliability, primarily aiming to enhance error correction and reduce transmission overhead. Current research heavily utilizes deep learning, employing architectures like LSTMs, autoencoders, and attention mechanisms to design robust non-linear codes adaptable to various channel conditions and noise levels. This work is significant for advancing communication systems, particularly in scenarios demanding ultra-reliable short-packet transmissions or those with noisy feedback channels, offering potential improvements in efficiency and robustness for applications like wireless networks and data storage.