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.

翻译：无线移动网络向云化演进的过程中，无线接入网络（RAN）功能可部署于集中或分布式位置，这带来了低成本部署、更高容量及硬件利用率提升等诸多优势。然而，功能部署的灵活性以严格的前传（FH）容量和延迟要求为代价。应对这些严苛约束的一种可行方法是采用前传压缩技术。为确保满足前传容量与延迟需求，在高负载时采用更强的前传压缩，而在中低负载时减少压缩以提升前传利用率和空口性能。本文提出一种基于无模型深度强化学习（DRL）的前传压缩（DRL-FC）框架，通过调制阶数、预编码器粒度及预编码器权重量化等影响前传负载与空口性能的多种配置参数，动态控制前传压缩。仿真结果表明，在不同前传负载水平下，相较于参考方案（即未应用压缩），DRL-FC展现出显著更高的前传利用率（平均68.7%）和空口吞吐量。同时，所提出的DRL-FC框架能够在各类前传负载下满足预设的前传延迟约束（本文设为260 $\mu$s）。