Name
Abstract | ||
|---|---|---|
As an efficient data fusion method, over-the-air computation integrates computation and communication by exploiting the superposition property of multiple access channels. In this paper, a framework on deep learning enabled over-the-air computation is proposed, where both the pre-processing and post-processing functions are represented by deep neural networks (DNNs). In this way, the over-the-air computation can approximate any function via learning through the data. The deep over-the-air framework is useful to a variety of machine learning applications on the Internet-of-Things (IoT). The experiments on distribution regression and anomaly detection have shown the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/GLOBECOM42002.2020.9321975 | GLOBECOM 2020 - 2020 IEEE Global Communications Conference |
Keywords | DocType | ISSN |
efficient data fusion method,deep learning,deep neural networks,over-the-air framework,deep over-the-air computation,DNN,machine learning applications,Internet-of-Things,IoT,distribution regression,anomaly detection | Conference | 1930-529X |
ISBN | Citations | PageRank |
978-1-7281-8299-5 | 1 | 0.38 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao Ye | 1 | 335 | 12.12 |
| Geoffrey Ye Li | 2 | 9071 | 660.27 |
| Biing-Hwang Juang | 3 | 3388 | 699.72 |