Autoencoding Graph Neural Networks for Scalable Transceiver Design

Autoencoder (AE) techniques have been intensively studied for the optimization of wireless transceivers. However, fixed computational structures of existing AE models lack the flexibility to the lengths of message bits and codewords. This work proposes a versatile AE framework, termed by autoencoding graph neural network (AEGNN), where both encoder and decoder are realized by GNNs. The viability of the proposed AEGNN is demonstrated in various application scenarios.