Open radio access network (O-RAN) architectures enable near real-time, software-driven control of network slicing through programmable xApps deployed on the near-real-time RAN Intelligent Controller (near-RT RIC). In industrial 5G downlink systems, adversarial jamming can abruptly reduce the effective physical resource block (PRB) capacity, triggering queue buildup and persistent latency violations, particularly in the presence of low spectral efficiency cell edge user equipments. This paper proposes a reserve-based resilience framework for PRB allocation in sliced O-RAN deployments. A finite pool of reserved PRBs is controlled by a near-RT RIC xApp that provides hybrid mitigation by proactively clearing backlog to build latency margin and reactively allocating reserve capacity during jammer active intervals. We formulate reserve activation as a constrained sequential decision problem and design a masked Deep Q-Network to learn effective control policies under non-stationary jamming. Simulation results show substantial reductions in URLLC latency violations and improved reserve efficiency compared to reactive baselines.

翻译：开放式无线接入网络（O-RAN）架构通过部署在近实时无线接入网络智能控制器（near-RT RIC）上的可编程xApp，实现了网络切片的近实时软件驱动控制。在工业5G下行系统中，恶意干扰会急剧降低有效物理资源块（PRB）容量，尤其在低频谱效率小区边缘用户设备存在时，将触发队列积压并导致持续性时延违规。本文提出了一种基于预留机制的弹性PRB分配框架，用于O-RAN切片部署场景。由near-RT RIC xApp管控的有限预留PRB池通过混合缓解策略实现抗干扰：主动清除积压以构建时延余量，并在干扰活跃期间被动分配预留容量。我们将预留激活建模为约束条件下的序贯决策问题，并设计掩蔽深度Q网络（Masked Deep Q-Network）以学习非平稳干扰环境下的有效控制策略。仿真结果表明，与被动式基准方案相比，所提方法显著降低了URLLC时延违规率并提升了预留效率。