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Learning-Based Latency-Constrained Fronthaul Compression Optimization in C-RAN

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.

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