  To improve the system performance towards the Shannon limit, advanced radio
resource management mechanisms play a fundamental role. In particular,
scheduling should receive much attention, because it allocates radio resources
among different users in terms of their channel conditions and QoS
requirements. The difficulties of scheduling algorithms are the tradeoffs need
to be made among multiple objectives, such as throughput, fairness and packet
drop rate. We propose a smart scheduling scheme based on deep reinforcement
learning (DRL). We not only verify the performance gain achieved, but also
provide implementation-friend designs, i.e., a scalable neural network design
for the agent and a virtual environment training framework. With the scalable
neural network design, the DRL agent can easily handle the cases when the
number of active users is time-varying without the need to redesign and retrain
the DRL agent. Training the DRL agent in a virtual environment offline first
and using it as the initial version in the practical usage helps to prevent the
system from suffering from performance and robustness degradation due to the
time-consuming training. Through both simulations and field tests, we show that
the DRL-based smart scheduling outperforms the conventional scheduling method
and can be adopted in practical systems.
