发表机构
Politecnico di Torino; Aalborg University; Chalmers University of Technology; University of Gothenburg（都灵理工大学; 奥尔堡大学; 查尔姆斯理工大学; 哥德堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对安全关键多模态推理中时间窗口约束下的无线调度问题，提出MIRA策略，基于条件证据增益与信道信息分配资源，实验显示相比仅相关性或仅信道调度显著提升准确率并降低误差。
AI 中文摘要
在自动驾驶和机器人导航等安全关键应用中，多模态推理要求异构传感器观测在任务规定的时间窗口内到达边缘服务器。参照被推断的事件或状态，该窗口终止于观测对当前决策仍保持有用的最晚时间。由于传感器就绪时间、数据量、无线信道条件和任务相关性因传感器而异，最大化网络吞吐量并不一定能在窗口关闭时最小化融合预测误差。我们将多模态上行调度视为顺序无线证据获取，对于线性最小均方误差（LMMSE）融合模型，从残差误差体积的减少中推导出条件证据增益度量。该度量通过二阶统计量定义，适用于高斯模型之外，当目标和预测误差联合高斯时，与条件互信息一致。随后，我们开发了MIRA，一种贪婪的任务驱动最大信息率分配策略，将条件证据增益与每个传感器的信道状态信息和剩余数据量相结合，同时在获取证据时更新传感器相关性。在合成分类、人类活动识别和车辆轨迹回归上的实验表明，MIRA优于仅相关性和仅信道调度。相对于前者，MIRA将分类准确率提高多达70%，并将回归均方误差（MSE）降低多达5.5%。相对于后者，MIRA达到80%目标准确率所需的获取时间减少58%，同时实现高达95%的更高准确率和9%的更低回归MSE。这些增益无需最大化接收数据量即可实现。
英文摘要
Multimodal inference in safety-critical applications, such as autonomous driving and robot navigation, requires heterogeneous sensor observations to reach an edge server within a task-prescribed temporal window. Referenced to the event or state being inferred, this window ends at the latest time an observation remains useful for the current decision. Since sensor readiness times, data volumes, wireless channel conditions, and task relevance vary across sensors, maximising network throughput does not necessarily minimise the fused prediction error when the window closes. We cast multimodal uplink scheduling as sequential wireless evidence acquisition and, for a linear minimum mean-square error (LMMSE) fusion model, derive a conditional evidence gain metric from the reduction in residual-error volume. Defined through second-order statistics, this metric applies beyond Gaussian models and coincides with conditional mutual information when the target and prediction errors are jointly Gaussian. We then develop MIRA, a greedy task-driven maximum information-rate allocation policy that combines conditional evidence gain with each sensor's channel state information and remaining data volume, while updating sensor relevance as evidence is acquired. Experiments on synthetic classification, human activity recognition, and vehicle-trajectory regression show that MIRA outperforms both relevance-only and channel-only scheduling. Relative to the former, MIRA improves classification accuracy by up to 70% and reduces regression mean-square error (MSE) by up to 5.5%. Relative to the latter, it requires 58% less acquisition time to attain an 80% target accuracy, while achieving up to 95% higher accuracy and 9% lower regression MSE. These gains are achieved without maximising received-data volume.
Comments5 pages, 4 figures, submitted to IEEE Communications letters
