The sensitivity of millimeter wave (mmWave) signals to blockages is a
fundamental challenge for mobile mmWave communication systems. The sudden
blockage of the line-of-sight (LOS) link between the base station and the
mobile user normally leads to disconnecting the communication session, which
highly impacts the system reliability. Further, reconnecting the user to
another LOS base station incurs high beam training overhead and critical
latency problems. In this paper, we leverage machine learning tools and propose
a novel solution for these reliability and latency challenges in mmWave MIMO
systems. In the developed solution, the base stations learn how to predict that
a certain link will experience blockage in the next few time frames using their
past observations of adopted beamforming vectors. This allows the serving base
station to proactively hand-over the user to another base station with a highly
probable LOS link. Simulation results show that the developed deep learning
based strategy successfully predicts blockage/hand-off in close to 95% of the
times. This reduces the probability of communication session disconnection,
which ensures high reliability and low latency in mobile mmWave systems.

使用机器学习工具，针对毫米波 MIMO 系统中基站和移动用户之间的可靠性和延迟挑战，提出了一种有效的解决方案，使基站能够学习如何预测下一个时间框架中某个链接将经历阻塞，并且根据其过去的观察结果主动将用户移交给具有高概率的 LOS 链接的另一个基站，从而保证了移动毫米波系统的高可靠性和低延迟。