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By implementing machine learning at the network edge, edge learning trainsmodels by leveraging rich data distributed at edge devices (e.g., smartphonesand sensors) and in return endow on them capabilities of seeing, listening, andreasoning. In edge learning, the need of high-mobility wireless dataacquisition arises in scenarios where edge devices (or even servers) aremounted on the ground or aerial vehicles. In this paper, we present a novelsolution, called fast analog transmission (FAT), for high- mobility dataacquisition in edge-learning systems, which has several key features. First,FAT incurs low-latency. Specifically, FAT requires no source-and-channel codingand no channel training via the proposed technique of Grassmann analog encoding(GAE) that encodes data samples into subspace matrices. Second, FAT supportsspatial multiplexing by directly transmitting analog vector data over anantenna array. Third, FAT can be seamlessly integrated with edge learning(i.e., training of a classifier model in this work). In particular, by applyinga Grassmannian-classification algorithm from computer vision, the received GAEencoded data can be directly applied to training the model without decoding andconversion. This design is found by simulation to outperform conventionalschemes in learning accuracy due to its robustness against data distortioninduced by fast fading.

Fast Analog Transmission for High-Mobility Wireless Data Acquisition in Edge Learning