Next-generation (NextG) cellular networks are designed to support emerging applications with diverse data rate and latency requirements, such as immersive multimedia services and large-scale Internet of Things deployments. A key enabling mechanism is radio access network (RAN) slicing, which dynamically partitions radio resources into virtual resource blocks to efficiently serve heterogeneous traffic classes, including enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC). In this paper, we study the impact of adversarial attacks on AI-driven RAN slicing decisions, where a budget-constrained adversary selectively jams slice transmissions to bias deep reinforcement learning (DRL)-based resource allocation, and quantify the resulting service level agreement (SLA) violations and post-attack recovery behavior. Our results indicate that budget-constrained adversarial jamming can induce severe and slice-dependent steady-state SLA violations. Moreover, the DRL agent's reward converges toward the clean baseline only after a non-negligible recovery period.

翻译：下一代（NextG）蜂窝网络旨在支持具有多样数据速率和延迟需求的新兴应用，例如沉浸式多媒体服务和大规模物联网部署。一个关键使能机制是无线接入网络（RAN）切片，它将无线资源动态划分为虚拟资源块，以高效服务异构流量类别，包括增强型移动宽带（eMBB）、大规模机器类通信（mMTC）和超可靠低延迟通信（URLLC）。在本文中，我们研究了对抗性攻击对AI驱动RAN切片决策的影响，其中受预算约束的对手通过选择性干扰切片传输以偏置基于深度强化学习（DRL）的资源分配，并量化由此产生的服务等级协议（SLA）违反行为及攻击后的恢复行为。我们的结果表明，受预算约束的对抗性干扰可导致严重且依赖切片的稳态SLA违反。此外，DRL代理的奖励仅在经过不容忽视的恢复期后才收敛至无攻击基准。