源熵引导的自适应传输用于通信驱动的多视角感知

中文摘要 AI 辅助

通信驱动的多视角感知依赖常规通信传输进行感知采集，而分布式设备上产生的感知数据必须在有限的通信资源下上传至边缘服务器。这造成了感知采集与边缘推理之间的独特耦合：通信间隔决定了源信息，而上行链路条件决定了有多少信息能够被传送至服务器以进行感知推理。为应对这一耦合，我们提出了一种源熵引导的自适应传输框架。具体而言，我们利用多输出高斯过程，将分组触发的信道状态信息（CSI）的熵表征为通信间隔的函数。所得解析界与由传输速率和时延要求决定的可用比特预算进行比较，以在原始数据传输与任务导向传输之间进行选择。对于任务导向传输，我们基于信息瓶颈构建了通信受限的推理问题，并将其分解为自适应分布式编码和多视角推理（ADE-MI），从而避免了设备与边缘服务器之间的交替优化。在Widar3.0多视角CSI手势识别数据集上的实验表明，解析界紧密跟随归一化流的数值估计，而ADE-MI在相同比特预算下优于任务导向的基准方法，且所提出的框架在时变信道下进一步提升了识别准确率。

英文摘要

Communication-driven multi-view sensing relies on routine communication transmissions for sensing acquisition, while the resulting sensing data at distributed devices must be uploaded to an edge server under limited communication resources. This creates a unique coupling between sensing acquisition and edge inference: the communication interval determines the source information, whereas the uplink condition determines how much information can be delivered to the server for sensing inference. To account for this coupling, we propose a source entropy-guided adaptive transmission framework. Specifically, we characterize the entropy of packet-triggered channel state information (CSI) as a function of the communication interval using a multi-output Gaussian process. The resulting analytical bound is compared with the available bit budget, determined by the transmission rate and latency requirement, to select between original-data and task-oriented transmission. For task-oriented transmission, we formulate the communication-constrained inference problem based on the information bottleneck and decompose it into adaptive distributed encoding and multi-view inference (ADE-MI), which avoids alternating optimization between the devices and the edge server. Experiments on the Widar3.0 multi-view CSI gesture recognition dataset show that the analytical bound closely follows the normalizing-flow numerical estimate, while ADE-MI outperforms task-oriented benchmarks under the same bit budget and the proposed framework further improves recognition accuracy under time-varying channels.

发表机构

• University of Glasgow（格拉斯哥大学）
• University of Aberdeen（阿伯丁大学）
• The University of Hong Kong（香港大学）

机构由 AI 辅助整理，请以论文原文为准。