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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.
By deploying machine-learning algorithms at the network edge, edge learning can leverage the enormous real-time data generated by billions of mobile devices to train AI models, which enable intelligent mobile applications. In this emerging research a
This letter studies a basic wireless caching network where a source server is connected to a cache-enabled base station (BS) that serves multiple requesting users. A critical problem is how to improve cache hit rate under dynamic content popularity.
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Deep learning (DL) based autoencoder is a promising architecture to implement end-to-end communication systems. One fundamental problem of such systems is how to increase the transmission rate. Two new schemes are proposed to address the limited data