Deep Over-the-Air Computation
Hao Ye, Geoffrey Ye Li, Biing‐Hwang Juang
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.