Machine learning (ML) has been widely used for efficient resource allocation (RA) in wireless networks. Although superb performance is achieved on small and simple networks, most existing ML-based approaches are confronted with difficulties when heterogeneity occurs and network size expands. In this paper, specifically focusing on power control/beamforming (PC/BF) in heterogeneous device-to-device (D2D) networks, we propose a novel unsupervised learning-based framework named heterogeneous interference graph neural network (HIGNN) to handle these challenges. First, we characterize diversified link features and interference relations with heterogeneous graphs. Then, HIGNN is proposed to empower each link to obtain its individual transmission scheme after limited information exchange with neighboring links. It is noteworthy that HIGNN is scalable to wireless networks of growing sizes with robust performance after trained on small-sized networks. Numerical results show that compared with state-of-the-art benchmarks, HIGNN achieves much higher execution efficiency while providing strong performance.

翻译：在无线网络中,机器学习(ML)被广泛用于高效的资源分配(RA),虽然在小型和简单网络上取得了超强性能,但大多数现有基于ML的方法在出现异质性和网络规模扩大时面临困难。在本文中,我们特别侧重于不同装置-装置-装置(D2D)网络中的权力控制/成型(PC/BF),我们提议建立一个新的、不受监督的学习基础框架,称为多式干扰图式神经网络(HIGNN),以应对这些挑战。首先,我们突出多样化的链接特征和与多元图形的干扰关系。然后,HIGN建议赋予每个链接在与相邻链接进行有限的信息交流后获得单个传输计划的能力。值得注意的是,HIGN在对小型网络进行培训后,可扩缩成规模且性能强的无线网络。数字结果显示,与最先进的基准相比,HIGN在提供强性能的同时,实现了更高的执行效率。