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.

翻译：在多用户通信系统中,用户排期和波束成型设计是两个基本问题,通常在现有文献中分别加以研究。在这项工作中,我们侧重于联合优化用户排期和波束设计,目标是尽可能扩大预定用户的既定基本特征。注意到由于非隐形客观功能以及连续和二进制变量的结合制约,这一问题在计算上具有挑战性。为了解决这些困难,我们首先建议采用迭代优化算法(IOA),依靠连续的螺旋近似和上链-下链链接双轨理论。然后,在IOA和图形神经网络的推动下,开发了一个联合用户排期和电源分配网络(JEPON),以便以不受监督的方式解决所调查的问题。各种数字结果可以核实IOA和JEPON的效力,后者的性能与IOA和贪婪的算法相比较低。值得注意的是,拟议的JEPON在动态无线网络情景的一般化能力方面也具有竞争力。