Constrained Deep Reinforcement Learning for Fronthaul Compression Optimization

TL;DR

This paper presents a deep reinforcement learning-based adaptive fronthaul compression scheme for C-RAN architectures, improving utilization while satisfying latency and packet loss constraints.

Contribution

It introduces a model-free, off-policy deep RL algorithm that is transparent and interpretable, tailored for optimizing fronthaul compression under strict constraints.

Findings

Achieves approximately 70% increase in fronthaul utilization.

Successfully satisfies latency and packet loss constraints.

Demonstrates effectiveness of model-free deep RL in network optimization.

Abstract

In the Centralized-Radio Access Network (C-RAN) architecture, functions can be placed in the central or distributed locations. This architecture can offer higher capacity and cost savings but also puts strict requirements on the fronthaul (FH). Adaptive FH compression schemes that adapt the compression amount to varying FH traffic are promising approaches to deal with stringent FH requirements. In this work, we design such a compression scheme using a model-free off policy deep reinforcement learning algorithm which accounts for FH latency and packet loss constraints. Furthermore, this algorithm is designed for model transparency and interpretability which is crucial for AI trustworthiness in performance critical domains. We show that our algorithm can successfully choose an appropriate compression scheme while satisfying the constraints and exhibits a roughly 70\% increase in FH utilization compared to a reference scheme.