@inproceedings{44c4651b6de14ef39c730eaa06fcb3bb,
title = "Autoencoder-based OFDM for Agricultural Image Transmission",
abstract = "With the rapid development of the Internet of Things (IoT), smart agricultural puts forward higher demands on the transmission of agricultural big data. This paper proposes an end-To-end learning communication with autoencoder-based orthogonal frequency division multiplexing (OFDM-AE) for agricultural image transmission, which solves the problems of delay, congestion and high complexity caused by the processing method to information of independent modularization for the conventional OFDM. It is proposed to construct AE based on convolutional neural network (CNN) to realize global joint optimization of end-To-end communication system. In this paper, the network architecture of OFDM-AE is designed and trained on massive agricultural image data. We analyze the performance of the proposed OFDM-AE in different signal-To-noise ratio (SNR) cases. The experimental results show that the OFDM-AE can retain the image feature information and has a very advantageous complexity performance compared to the conventional OFDM with various modulation methods.",
keywords = "Internet of Things, OFDM, agricultural image transmission, autoencoder, big data",
author = "Dongbo Li and Xiangyu Liu and Yuxuan Shao and Yuchen Sun and Siyao Cheng and Jie Liu",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 10th International Conference on Advanced Cloud and Big Data, CBD 2022 ; Conference date: 04-11-2022 Through 05-11-2022",
year = "2022",
doi = "10.1109/CBD58033.2022.00036",
language = "英语",
series = "Proceedings - 2022 10th International Conference on Advanced Cloud and Big Data, CBD 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "157--162",
booktitle = "Proceedings - 2022 10th International Conference on Advanced Cloud and Big Data, CBD 2022",
address = "美国",
}