AI 中文总结
该研究提出面向无线边云推理的任务感知语义分割学习框架，采用MI驱动的UEP为关键潜在分量分配更高功率，在物联网感知数据实验中优于EEP和固定UEP基线，适用性广泛。
AI 中文摘要
我们提出了一种面向无线边云推理的任务感知语义分割学习（SL）框架，其中传输的潜在表示的可靠性会根据其对下游任务的相关性进行动态调整。基于自动编码器（AE）的物理（PHY）层实现了通信接口的端到端学习，而不等错误保护（UEP）则通过训练期间基于互信息（MI）的潜在分量优先级排序来实现。估计的MI相对于每个潜在分量的梯度作为基于敏感性的任务相关性代理，提供了一种完全由学习驱动的优先级排序，该排序既适应数据分布，也适应下游任务。我们进一步表明，这种优先级排序会转化为可测量的物理层效应：与等错误保护（EEP）基线相比，MI引导的UEP为对任务最关键的潜在分量分配了显著更高的发射功率。在真实物联网感知数据上的实验表明，在不同信噪比（SNR）范围内，与等错误保护和固定UEP基线相比，该方法始终能取得增益。额外分析证实了排名稳定性、估计器鲁棒性以及跨数据集和任务类型的泛化能力，表明所提出的框架具有广泛的适用性。
英文摘要
We propose a task-aware semantic split learning (SL) framework for wireless edge-cloud inference, in which the reliability of transmitted latent representations is dynamically adapted to their relevance for the downstream task. An autoencoder (AE)-based physical (PHY) layer enables end-to-end learning of the communication interface, while unequal error protection (UEP) is realized via mutual information (MI)-driven prioritization of latent components during training. The gradient of the estimated MI with respect to each latent component serves as a sensitivity-based proxy for task relevance, providing a fully learning-driven prioritization that adapts to both the data distribution and the downstream task. We further show that this prioritization translates into measurable physical-layer effects: MI-guided UEP assigns significantly higher transmit power to the most task-critical latent components compared to the equal error protection (EEP) baseline. Experiments on real-world IoT sensing data demonstrate consistent gains over equal and fixed-UEP baselines across SNR regimes. Additional analysis confirms ranking stability, estimator robustness and generalization across datasets and task types, indicating broad applicability of the proposed framework.
CommentsAccepted for publication at IEEE Communications Letters
