The edge artificial intelligence (AI) applications in next-generation mobile networks demand efficient AI-model downloading techniques to support real-time, on-device inference. However, transmitting high-dimensional AI models over wireless channels remains challenging due to limited communication resources. To address this issue, we propose a parametric-sensitivity-aware retransmission (PASAR) framework that manages radio-resource usage of different parameter packets according to their importance on model inference accuracy, known as parametric sensitivity. Empirical analysis reveals a highly right-skewed sensitivity distribution, indicating that only a small fraction of parameters significantly affect model performance. Leveraging this insight, we design a novel online retransmission protocol, i.e., the PASAR protocol, that adaptively terminates packet transmission based on real-time bit error rate (BER) measurements and the associated parametric sensitivity. The protocol employs an adaptive, round-wise stopping criterion, enabling heterogeneous, packet-level retransmissions that preserve overall model functionality but reduce overall latency. Extensive experiments across diverse deep neural network architectures and real-world datasets demonstrate that PASAR substantially outperforms classical hybrid automatic repeat request (HARQ) schemes in terms of communication efficiency and latency.

翻译：下一代移动网络中的边缘人工智能（AI）应用需要高效的AI模型下载技术以支持实时、设备端推理。然而，由于通信资源有限，在无线信道上传输高维AI模型仍面临挑战。为解决这一问题，我们提出了一种参数敏感性感知重传（PASAR）框架，该框架根据不同参数包对模型推理精度的重要性（即参数敏感性）来管理其无线资源使用。实证分析揭示了高度右偏的敏感性分布，表明仅有一小部分参数会显著影响模型性能。基于这一发现，我们设计了一种新颖的在线重传协议——PASAR协议，该协议根据实时误码率（BER）测量值及相关参数敏感性自适应地终止数据包传输。该协议采用自适应的轮次停止准则，实现了异构的、数据包级别的重传，在保持模型整体功能的同时降低了总体延迟。在多种深度神经网络架构和真实数据集上进行的大量实验表明，PASAR在通信效率和延迟方面显著优于传统的混合自动重传请求（HARQ）方案。