Policy Distillation for Real-Time Inference in Fronthaul Congestion Control

Centralized Radio Access Networks (C-RANs) are improving their cost-efficiency through packetized fronthaul networks. Such a vision requires network congestion control algorithms to deal with sub-millisecond delay budgets while optimizing link utilization and fairness. Classic congestion control algorithms have struggled to optimize these goals simultaneously in such scenarios. Therefore, many Reinforcement Learning (RL) approaches have recently been proposed to deal with such limitations. However, when considering RL policies’ deployment in the real world, many challenges exist. This paper deals with the real-time inference challenge, where a deployed policy has to output actions in microseconds. The experiments here evaluate the tradeoff of inference time and performance regarding a TD3 (Twin-delayed Deep Deterministic Policy Gradient) policy baseline and simpler Decision Tree (DT) policies extracted from TD3 via a process of policy distillation. The results indicate that DTs with a suitable depth can maintain performances similar to those of the TD3 baseline. Additionally, we show that by converting the distilled DTs to rules in C++, we can make inference-time nearly negligible, i.e., sub-microsecond time scale. The proposed method enables the use of state-of-the-art RL techniques to congestion control scenarios with tight inference-time and computational constraints.