Adaptive transmission improves multi-view sensing under limited communication
Source Entropy-Guided Adaptive Transmission for Communication-Driven Multi-View Sensing
Information Theory
Summary
Devices that sense the environment using multiple viewpoints need to send data to a central server, but communication limits how much data can be sent. The authors analyze how often devices communicate and how much data should be sent to balance quality and speed. They introduce a method that decides when to send raw data or compressed task-focused data, based on the available communication bandwidth. Their approach improves gesture recognition accuracy even when network conditions change over time.
What this means in practice
- •For network schedulers: Optimize uplink transmissions for edge servers by adapting data sent from multiple sensors under varying bandwidth and latency constraints.
- •For mobile app developers: Help apps that recognize gestures from multiple devices maintain accuracy when network conditions fluctuate.
Tested on one dataset.
Authors
Mingjie Yang, Guangming Liang, Dongzhu Liu, Lei Zhang, Xiaonan Liu, Kaibin Huang
Abstract
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.