  Millimeter Wave (MmWave) communication is one of the key technology of the
fifth generation (5G) wireless systems to achieve the expected 1000x data rate.
With large bandwidth at mmWave band, the link capacity between users and base
stations (BS) can be much higher compared to sub-6GHz wireless systems.
Meanwhile, due to the high cost of infrastructure upgrade, it would be
difficult for operators to drastically enhance the capacity of backhaul links
between mmWave BSs and the core network. As a result, the data rate provided by
backhaul may not be sufficient to support all mmWave links, the backhaul
connection becomes the new bottleneck that limits the system performance. On
the other hand, as mmWave channels are subject to random blockage, the data
rates of mmWave users significantly vary over time. With limited backhaul
capacity and highly dynamic data rates of users, how to allocate backhaul
resource to each user remains a challenge for mmWave systems. In this article,
we present a deep reinforcement learning (DRL) approach to address this
challenge. By learning the blockage pattern, the system dynamics can be
captured and predicted, resulting in efficient utilization of backhaul
resource. We begin with a discussion on DRL and its application in wireless
systems. We then investigate the problem backhaul resource allocation and
present the DRL based solution. Finally, we discuss open problems for future
research and conclude this article.
