  As the number of user equipments (UEs) with various data rate and latency
requirements increases in wireless networks, the resource allocation problem
for orthogonal frequency-division multiple access (OFDMA) becomes challenging.
In particular, varying requirements lead to a non-convex optimization problem
when maximizing the systems data rate while preserving fairness between UEs. In
this paper, we solve the non-convex optimization problem using deep
reinforcement learning (DRL). We outline, train and evaluate a DRL agent, which
performs the task of media access control scheduling for a downlink OFDMA
scenario. To kickstart training of our agent, we introduce mimicking learning.
For improvement of scheduling performance, full buffer state information at the
base station (e.g. packet age, packet size) is taken into account. Techniques
like input feature compression, packet shuffling and age capping further
improve the performance of the agent. We train and evaluate our agents using
Nokia's wireless suite and evaluate against different benchmark agents. We show
that our agents clearly outperform the benchmark agents.
