  In next-generation communications, sub-6GHz and millimeter-wave (mmWave)
links typically coexist, with the sub-6GHz link always active and the mmWave
link active when high-rate transmission is required. Due to the spatial
similarities between sub-6GHz and mmWave channels, sub-6GHz channel information
can be utilized to support hybrid beamforming in mmWave communications to
reduce overhead costs. We consider a multi-cell heterogeneous communication
network where both sub-6GHz and mmWave communications co-exist. Multiple mmWave
base stations (BSs) in the heterogeneous network simultaneously transmit
signals to multiple users in their own mmWave cells while interfering with each
other. The challenging problem is to design hybrid beamformers in the mmWave
band that can maximize the system spectral efficiency. To address this highly
complex programming using sub-6GHz information, a novel heterogeneous graph
neural network (HGNN) architecture is proposed to learn the intrinsic
relationship between sub-6GHz and mmWave and design the hybrid beamformers for
mmWave BSs. The proposed HGNN consists of two different node types, namely, BS
nodes and user equipment (UE) nodes, and two different edge types, namely,
desired link edge and interfering link edge. In addition, the attention
mechanism and the residual structure are utilized in the HGNN architecture to
improve the performance. Simulation results show that the proposed HGNN can
successfully achieve better performances with sub-6GHz information than
traditional learning methods. The results also demonstrate that the attention
mechanism and residual structure improve the performances of the HGNN compared
to its unmodified counterparts.
