  The evolution of wireless mobile networks towards cloudification, where Radio
Access Network (RAN) functions can be hosted at either a central or distributed
locations, offers many benefits like low cost deployment, higher capacity, and
improved hardware utilization. Nevertheless, the flexibility in the functional
deployment comes at the cost of stringent fronthaul (FH) capacity and latency
requirements. One possible approach to deal with these rigorous constraints is
to use FH compression techniques. To ensure that FH capacity and latency
requirements are met, more FH compression is applied during high load, while
less compression is applied during medium and low load to improve FH
utilization and air interface performance. In this paper, a model-free deep
reinforcement learning (DRL) based FH compression (DRL-FC) framework is
proposed that dynamically controls FH compression through various configuration
parameters such as modulation order, precoder granularity, and precoder weight
quantization that affect both FH load and air interface performance. Simulation
results show that DRL-FC exhibits significantly higher FH utilization (68.7% on
average) and air interface throughput than a reference scheme (i.e. with no
applied compression) across different FH load levels. At the same time, the
proposed DRL-FC framework is able to meet the predefined FH latency constraints
(in our case set to 260 $\mu$s) under various FH loads.
