为工业5G低时延切片设计抗干扰资源预留机制，提升网络稳定性。
Jamming-Resilient PRB Reservation for Latency-Critical O-RAN Network Slicing
- 通过预留物理资源块，结合主动清空队列与被动分配策略应对干扰
- 相比传统方法，超可靠低时延通信时延违规率显著下降，预留效率更高
- 适合对时延敏感的工业物联网场景，尤其应对边缘用户弱信号问题
开放无线接入网（O-RAN）架构通过近实时的RAN智能控制器（near-RT RIC）部署可编程xApp，实现网络切片的软件化控制。在工业5G下行系统中，恶意干扰会突然降低有效物理资源块（PRB）容量，导致队列堆积和持续的时延违规，尤其在小区边缘用户设备谱效较低时更为严重。本文提出一种基于预留的抗干扰PRB分配框架，由near-RT RIC xApp管理有限的预留PRB池，通过主动清除积压以建立时延余量，并在干扰活跃时段被动分配预留资源实现混合缓解。将预留激活建模为约束序列决策问题，设计掩码深度Q网络（masked DQN）以学习非平稳干扰下的有效控制策略。仿真结果表明，相比反应式基线方法，该方案显著降低了URLLC时延违规率，同时提升了预留资源利用效率。
原文摘要 · Abstract (English)
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.
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