用数字孪生+生成AI训练边缘智能，提升战场网络抗干扰能力
EdgeAgentX-DT: Integrating Digital Twins and Generative AI for Resilient Edge Intelligence in Tactical Networks
- 构建数字孪生环境，实现真实设备与虚拟模型同步
- 生成对抗性场景，使智能体训练更鲁棒，收敛更快
- 适合军事边缘计算、高对抗环境下的AI系统设计
我们提出EdgeAgentX-DT，是EdgeAgentX框架的增强版，通过集成数字孪生仿真与生成式AI驱动的场景训练，显著提升军事网络中的边缘智能。该系统利用与真实边缘设备同步的网络数字孪生，提供安全、逼真的训练与验证环境。借助扩散模型和变压器等生成式AI方法，系统可生成多样化且具有对抗性的训练场景，以强化模拟训练效果。其多层架构包括：（1）设备端边缘智能；（2）数字孪生同步；（3）生成式场景训练。实验仿真显示，相较于EdgeAgentX，EdgeAgentX-DT在学习收敛速度、网络吞吐量、延迟降低以及抗干扰和节点失效方面均有显著提升。一项复杂战术场景案例研究显示，在同时面临干扰攻击、代理故障和网络负载增加的情况下，EdgeAgentX-DT仍能维持运行性能，而基线方法则失效。结果表明，基于数字孪生的生成式训练可有效增强边缘AI在对抗环境中的部署韧性。
原文摘要 · Abstract (English)
We introduce EdgeAgentX-DT, an advanced extension of the EdgeAgentX framework that integrates digital twin simulations and generative AI-driven scenario training to significantly enhance edge intelligence in military networks. EdgeAgentX-DT utilizes network digital twins, virtual replicas synchronized with real-world edge devices, to provide a secure, realistic environment for training and validation. Leveraging generative AI methods, such as diffusion models and transformers, the system creates diverse and adversarial scenarios for robust simulation-based agent training. Our multi-layer architecture includes: (1) on-device edge intelligence; (2) digital twin synchronization; and (3) generative scenario training. Experimental simulations demonstrate notable improvements over EdgeAgentX, including faster learning convergence, higher network throughput, reduced latency, and improved resilience against jamming and node failures. A case study involving a complex tactical scenario with simultaneous jamming attacks, agent failures, and increased network loads illustrates how EdgeAgentX-DT sustains operational performance, whereas baseline methods fail. These results highlight the potential of digital-twin-enabled generative training to strengthen edge AI deployments in contested environments.
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