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In this paper, we consider the problem of joint beam selection and linkactivation across a set of communication pairs to effectively control theinterference between communication pairs via inactivating part communicationpairs in ultra-dense device-to-device (D2D) mmWave communication networks. Theresulting optimization problem is formulated as an integer programming problemthat is nonconvex and NP-hard. Consequently, the global optimal solution, eventhe local optimal solution, cannot be generally obtained. To overcome thischallenge, this paper resorts to design a deep learning architecture based ongraph neural network to finish the joint beam selection and link activation,with taking the network topology information into account. Meanwhile, wepresent an unsupervised Lagrangian dual learning framework to train theparameters of the GBLinks model. Numerical results show that the proposedGBLinks model can converges to a stable point with the number of iterationsincreases, in terms of the weighted sum rate. Furthermore, the GBLinks modelcan reach near-optimal solution through comparing with the exhaustive searchscheme in small-scale ultra-dense D2D mmWave communication networks andoutperforms GreedyNoSched and the SCA-based method. It also shows that theGBLinks model can generalize to varying densities and coverage regions ofultra-dense D2D mmWave communication networks.

GBLinks: GNN-Based Beam Selection and Link Activation for Ultra-dense D2D mmWave Networks