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

TL;DR

This paper introduces FAT, a low-latency, high-mobility wireless data acquisition method for edge learning that uses Grassmann analog encoding to transmit data directly over antenna arrays, enhancing robustness and training efficiency.

Contribution

The paper proposes FAT, a novel analog transmission scheme with Grassmann encoding for high-mobility edge learning, eliminating the need for source/channel coding and enabling direct model training.

Findings

FAT outperforms conventional schemes in learning accuracy.

FAT is robust against data distortion from fast fading.

Supports seamless integration with edge learning models.

Abstract

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.