Joint User Scheduling and Beamforming Design for Multiuser MISO Downlink Systems

In multiuser communication systems, user scheduling and beamforming design are two fundamental problems, which are usually investigated separately in the existing literature. In this work, we focus on the joint optimization of user scheduling and beamforming design with the goal of maximizing the set cardinality of scheduled users. Observing that this problem is computationally challenging due to the non-convex objective function and coupled constraints in continuous and binary variables. To tackle these difficulties, we first propose an iterative optimization algorithm (IOA) relying on the successive convex approximation and uplink-downlink duality theory. Then, motivated by IOA and graph neural networks, a joint user scheduling and power allocation network (JEEPON) is developed to address the investigated problem in an unsupervised manner. The effectiveness of IOA and JEEPON is verified by various numerical results, and the latter achieves a close performance but lower complexity compared with IOA and the greedy-based algorithm. Remarkably, the proposed JEEPON is also competitive in terms of the generalization ability in dynamic wireless network scenarios.

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

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 problem. 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 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 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 scales and densities of ultra-dense D2D mmWave communication networks.