  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.
