Unsupervised Learning-Based Coordinated Hybrid Precoding for MmWave Massive MIMO-Enabled HetNets

Hybrid precoding has been recognized as promising and effective for practical 5G communication. It is generally challenging to obtain the sample for deep learning-based hybrid precoding due to its need of massive precoding vector and channel matrix. To effectively solve this issue, a novel coordinated hybrid precoding algorithm based on unsupervised learning graph attention networks (CHP-ULGAT) is first developed by making full use of the underlying topology formed by the channel matrix. Subsequently, a more realistic situation of existing an ultra-low execution time is considered. A sub-optimal coordinated hybrid precoding based on unsupervised learning convolutional neural networks (CHP-ULCNN) is proposed to further reduce complexity. Moreover, we present effective ways to design the multi-matrix operation and the loss function to address the practicability of the algorithm. Extensive simulation results show that the proposed hybrid-precoding algorithms have obvious advantages in spectral efficiency (SE) and energy efficiency (EE) improvement with ultra-low computational complexity, considering the different number of RF chains and deployment scenarios.