  Scheduling plays a pivotal role in multi-user wireless communications, since
the quality of service of various users largely depends upon the allocated
radio resources. In this paper, we propose a novel scheduling algorithm with
contiguous frequency-domain resource allocation (FDRA) based on deep
reinforcement learning (DRL) that jointly selects users and allocates resource
blocks (RBs). The scheduling problem is modeled as a Markov decision process,
and a DRL agent determines which user and how many consecutive RBs for that
user should be scheduled at each RB allocation step. The state space, action
space, and reward function are delicately designed to train the DRL network.
More specifically, the originally quasi-continuous action space, which is
inherent to contiguous FDRA, is refined into a finite and discrete action space
to obtain a trade-off between the inference latency and system performance.
Simulation results show that the proposed DRL-based scheduling algorithm
outperforms other representative baseline schemes while having lower online
computational complexity.
