Proactive Hybrid Precoding for Time-varying mmWave Channel with Deep Learning

Hybrid precoding can support high data rate with low cost for millimeter wave (mmWave) multi-antenna systems. To achieve near-optimal performance with low computing latency and enable end-to-end learning, deep learning has been introduced for optimizing hybrid precoding. Most research efforts focus on learning hybrid precoding under static channels. In mobile communications, however, the channel aging effect incurs severe performance degradation of multi-antenna systems. In this paper, we resort to a proactive optimization method to learn the analog and digital precoders for multiple users in time-varying mmWave channels with implicit channel prediction. We consider the practical frame structure used in prevalent cellular systems, and propose a method to learn the hybrid precoding polices for multiple downlink subframes in parallel. Simulation results demonstrate that the proposed method performs closely to the hybrid precoding that assuming perfect future channel information and outperforms existing methods.