Open Radio Access Networks (O-RAN) are increasingly adopting data-driven control through Deep Reinforcement Learning (DRL) to optimize complex tasks such as network slicing and mobility management. However, the deployment of DRL in carrier-grade networks is hindered by its inherent opacity and stochastic execution, which limit operator trust, auditability, and safe deployment. Existing explainable AI (XAI) approaches primarily provide post-hoc insights and fail to produce executable, interpretable policies suitable for operational environments. In this paper, we present DeRAN, a neuro-symbolic framework that bridges the gap between DRL performance and operational transparency by distilling black-box DRL policies into human-readable symbolic representations. DeRAN introduces a concept-driven abstraction layer that transforms high-dimensional network telemetry into a compact set of semantically meaningful features, enabling interpretable policy learning. Building on the semantically grounded concepts, DeRAN synthesizes symbolic policies using deep symbolic regression (DSR) for continuous control and neurally guided differentiable logic (NUDGE) for discrete decision-making. We implement DeRAN on a live 5G O-RAN testbed and evaluate it on two representative use cases. Experimental results demonstrate that DeRAN achieves 78\% and 87\% of DRL's cumulative rewards in the two use cases, while offering interpretability and auditability by design. Source code is available at https://github.com/Jadejavu/A-Neuro-Symbolic-Framework-for-Interpretable-Open-RAN-Automation

翻译：开放式无线接入网络（O-RAN）正日益采用通过深度强化学习（DRL）的数据驱动控制来优化网络切片和移动性管理等复杂任务。然而，DRL在运营商级网络中的部署因其固有的不透明性和随机执行而受阻，这限制了运营商的信任、可审计性和安全部署。现有的可解释人工智能（XAI）方法主要提供事后解释，无法生成适用于操作环境的可执行、可解释策略。在本文中，我们提出DeRAN，一个神经符号框架，通过将黑盒DRL策略蒸馏为人类可读的符号表示，弥合DRL性能与操作透明度之间的差距。DeRAN引入了一种概念驱动的抽象层，将高维网络遥测数据转化为一组紧凑的语义有意义特征，从而实现可解释的策略学习。基于语义基础概念，DeRAN使用深度符号回归（DSR）合成连续控制的符号策略，并使用神经引导可微逻辑（NUDGE）合成离散决策的符号策略。我们在实时5G O-RAN测试平台上实现DeRAN，并在两个代表性用例上进行评估。实验结果表明，DeRAN在两个用例中分别达到DRL累积奖励的78%和87%，同时提供了设计的可解释性和可审计性。源代码可在https://github.com/Jadejavu/A-Neuro-Symbolic-Framework-for-Interpretable-Open-RAN-Automation获取。