  Link scheduling in device-to-device (D2D) networks is usually formulated as a
non-convex combinatorial problem, which is generally NP-hard and difficult to
get the optimal solution. Traditional methods to solve this problem are mainly
based on mathematical optimization techniques, where accurate channel state
information (CSI), usually obtained through channel estimation and feedback, is
needed. To overcome the high computational complexity of the traditional
methods and eliminate the costly channel estimation stage, machine leaning (ML)
has been introduced recently to address the wireless link scheduling problems.
In this paper, we propose a novel graph embedding based method for link
scheduling in D2D networks. We first construct a fully-connected directed graph
for the D2D network, where each D2D pair is a node while interference links
among D2D pairs are the edges. Then we compute a low-dimensional feature vector
for each node in the graph. The graph embedding process is based on the
distances of both communication and interference links, therefore without
requiring the accurate CSI. By utilizing a multi-layer classifier, a scheduling
strategy can be learned in a supervised manner based on the graph embedding
results for each node. We also propose an unsupervised manner to train the
graph embedding based method to further reinforce the scalability and
generalizability and develop a K-nearest neighbor graph representation method
to reduce the computational complexity. Extensive simulation demonstrates that
the proposed method is near-optimal compared with the existing state-of-art
methods but is with only hundreds of training samples. It is also competitive
in terms of scalability and generalizability to more complicated scenarios.
