面向基于深度学习的干扰的、借助诱饵辅助的下一代（NextG）及时通信
Timely NextG Communications with Decoy Assistance against Deep Learning-based Jamming
AI总结：
针对受深度学习窃听者干扰的下一代通信时间敏感信息传输问题，提出基于诱饵的抗干扰策略，分析信息年龄与功率控制影响，验证了用诱饵保障及时传输、缓解攻击的可行性。
摘要：
我们研究了在存在具备干扰检测到的传输能力的深度学习窃听者、且受平均功率预算约束的场景下，下一代（NextG）通信系统中时间敏感信息的传输问题。本文提出了一种基于诱饵的抗干扰策略，用于迷惑干扰者，使其在干扰诱饵消息而非真实消息时浪费功率。我们研究了该抗干扰策略在保障可靠性目标之外，保障NextG通信时效性的效果，分析了受干扰和信道效应影响的信息年龄（Age of Information）。我们评估了功率控制的影响：功率控制决定了传输的成功率，但也会影响对手检测的准确性，使干扰者更有可能成功识别并干扰通信。结果表明，在以信息新鲜度为目标的NextG通信中，使用诱饵保障及时信息传输来缓解窃听和干扰攻击是可行的。
英文摘要：
We consider the transfer of time-sensitive information in next-generation (NextG) communication systems in the presence of a deep learning based eavesdropper capable of jamming detected transmissions, subject to an average power budget. A decoy-based anti-jamming strategy is presented to confuse a jammer, causing it to waste power when disrupting decoy messages instead of real messages. We investigate the effectiveness of the anti-jamming strategy to guarantee timeliness of NextG communications in addition to reliability objectives, analyzing the Age of Information subject to jamming and channel effects. We assess the effect of power control, which determines the success of a transmission but also affects the accuracy of the adversary's detection, making it more likely for the jammer to successfully identify and jam the communication. The results demonstrate the feasibility of mitigating eavesdropping and jamming attacks in NextG communications with information freshness objectives using a decoy to guarantee timely information transfer.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
