  By implementing machine learning at the network edge, edge learning trains
models by leveraging rich data distributed at edge devices (e.g., smartphones
and sensors) and in return endow on them capabilities of seeing, listening, and
reasoning. In edge learning, the need of high-mobility wireless data
acquisition arises in scenarios where edge devices (or even servers) are
mounted on the ground or aerial vehicles. In this paper, we present a novel
solution, called fast analog transmission (FAT), for high- mobility data
acquisition in edge-learning systems, which has several key features. First,
FAT incurs low-latency. Specifically, FAT requires no source-and-channel coding
and no channel training via the proposed technique of Grassmann analog encoding
(GAE) that encodes data samples into subspace matrices. Second, FAT supports
spatial multiplexing by directly transmitting analog vector data over an
antenna array. Third, FAT can be seamlessly integrated with edge learning
(i.e., training of a classifier model in this work). In particular, by applying
a Grassmannian-classification algorithm from computer vision, the received GAE
encoded data can be directly applied to training the model without decoding and
conversion. This design is found by simulation to outperform conventional
schemes in learning accuracy due to its robustness against data distortion
induced by fast fading.
