  In this paper, we consider the problem of joint beam selection and link
activation across a set of communication pairs to effectively control the
interference between communication pairs via inactivating part communication
pairs in ultra-dense device-to-device (D2D) mmWave communication networks. The
resulting optimization problem is formulated as an integer programming problem
that is nonconvex and NP-hard. Consequently, the global optimal solution, even
the local optimal solution, cannot be generally obtained. To overcome this
challenge, this paper resorts to design a deep learning architecture based on
graph neural network to finish the joint beam selection and link activation,
with taking the network topology information into account. Meanwhile, we
present an unsupervised Lagrangian dual learning framework to train the
parameters of the GBLinks model. Numerical results show that the proposed
GBLinks model can converges to a stable point with the number of iterations
increases, in terms of the weighted sum rate. Furthermore, the GBLinks model
can reach near-optimal solution through comparing with the exhaustive search
scheme in small-scale ultra-dense D2D mmWave communication networks and
outperforms GreedyNoSched and the SCA-based method. It also shows that the
GBLinks model can generalize to varying densities and coverage regions of
ultra-dense D2D mmWave communication networks.
