Efficient Fusion and Reconstruction for Communication and Sensing Signals in Green IoT Networks

Abstract

Efficient and green transmission of communication and sensing (C&S) signals is a vital problem in Internet of Things (IoT) networks. In this article, we propose a variational autoencoder (VAE)-empowered deep learning (DL) network to fuse and reconstruct the integrated C&S signals. Specifically, we present a convolutional neural network to fuse the input communication data and SAR images into a combined representation, which can then be transmitted to other nodes in space–air–ground–ocean-integrated IoT networks. Instead of directly transmitting C&S data, the transmission of a fused feature vector can greatly save network resources and reduce network burden. Then, a mirrored deconvolutional network is constructed to recover C&S data from the transmitted feature representation. An end-to-end unsupervised training strategy is considered to train the proposed DL network without any label information and human labor. Qualitative and quantitative experiments demonstrate the feasibility of our proposed approach for transmitting and reconstructing integrated C&S signals. Further analysis on hyperparameter sensitivity and loss functions verifies the necessity and efficiency of the components in the proposed DL model. Note that the proposed efficient fusion and reconstruction schemes for C&S signals may provide the convenience to information sharing under the distributed scenarios.