  Spectrum management and resource allocation (RA) problems are challenging and
critical in a vast number of research areas such as wireless communications and
computer networks. The traditional approaches for solving such problems usually
consume time and memory, especially for large size problems. Recently different
machine learning approaches have been considered as potential promising
techniques for combinatorial optimization problems, especially the generative
model of the deep neural networks. In this work, we propose a resource
allocation deep autoencoder network, as one of the promising generative models,
for enabling spectrum sharing in underlay device-to-device (D2D) communication
by solving linear sum assignment problems (LSAPs). Specifically, we investigate
the performance of three different architectures for the conditional
variational autoencoders (CVAE). The three proposed architecture are the
convolutional neural network (CVAE-CNN) autoencoder, the feed-forward neural
network (CVAE-FNN) autoencoder, and the hybrid (H-CVAE) autoencoder. The
simulation results show that the proposed approach could be used as a
replacement of the conventional RA techniques, such as the Hungarian algorithm,
due to its ability to find solutions of LASPs of different sizes with high
accuracy and very fast execution time. Moreover, the simulation results reveal
that the accuracy of the proposed hybrid autoencoder architecture outperforms
the other proposed architectures and the state-of-the-art DNN techniques.
