将AI模型反馈纳入闭环控制，提升边缘智能的通信与感知效率。
AI-in-the-Loop Sensing and Communication Joint Design for Edge Intelligence
- 用AI驱动闭环控制，动态优化数据采集与通信参数。
- 通信能耗降低77%，采样次数减少52%，验证损失下降58%。
- 适合关注边缘计算、AI与通信协同优化的研究者。
人工智能、无线通信与感知技术的突破推动了边缘智能的发展。然而，传统系统仍存在通信效率低、数据冗余和模型泛化能力差等问题。为此，本文提出一种AI-in-the-loop联合感知与通信（JSAC）框架，通过AI驱动的闭环控制架构联合优化系统资源，显著提升整体性能。核心贡献在于建立验证损失与可调参数之间的显式关系，使模型可通过闭环反馈动态降低泛化误差。在感知方面，提出基于梯度重要性采样的自适应数据采集策略，使边缘设备根据实时模型反馈自主决定停止采集时机并分配样本权重。在通信方面，借鉴随机梯度朗之万动力学（SGLD），联合优化发射功率与批大小，将信道与数据噪声转化为梯度扰动，缓解过拟合。实验表明，该框架可实现最高77%的通信能耗降低、52%的采样成本削减，且验证损失最多降低58%，验证了将模型自身融入系统控制环路能带来AI与JSAC系统的协同增益。
原文摘要 · Abstract (English)
Recent breakthroughs in artificial intelligence (AI), wireless communications, and sensing technologies have accelerated the evolution of edge intelligence. However, conventional systems still grapple with issues such as low communication efficiency, redundant data acquisition, and poor model generalization. To overcome these challenges, we propose an innovative framework that enhances edge intelligence through AI-in-the-loop joint sensing and communication (JSAC). This framework features an AI-driven closed-loop control architecture that jointly optimizes system resources, thereby delivering superior system-level performance. A key contribution of our work is establishing an explicit relationship between validation loss and the system's tunable parameters. This insight enables dynamic reduction of the generalization error through AI-driven closed-loop control. Specifically, for sensing control, we introduce an adaptive data collection strategy based on gradient importance sampling, allowing edge devices to autonomously decide when to terminate data acquisition and how to allocate sample weights based on real-time model feedback. For communication control, drawing inspiration from stochastic gradient Langevin dynamics (SGLD), our joint optimization of transmission power and batch size converts channel and data noise into gradient perturbations that help mitigate overfitting. Experimental evaluations demonstrate that our framework reduces communication energy consumption by up to 77 percent and sensing costs measured by the number of collected samples by up to 52 percent while significantly improving model generalization -- with up to 58 percent reductions of the final validation loss. It validates that the proposed scheme can harvest the mutual benefit of AI and JSAC systems by incorporating the model itself into the control loop of the system.
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