CSI2Image: Image Reconstruction From Channel State Information Using Generative Adversarial Networks
CSI2Image: Image Reconstruction From Channel State Information Using Generative Adversarial Networks
复制标题
DOI：
10.1109/access.2021.3066158
复制
发表时间：
2020-09
期刊：
影响因子：
3.9
通讯作者：
Sorachi Kato;Takeru Fukushima;T. Murakami;H. Abeysekera;Yusuke Iwasaki;T. Fujihashi;Takashi Watanabe;S. Saruwatari
中科院分区：
文献类型：
--
作者：
Sorachi Kato;Takeru Fukushima;T. Murakami;H. Abeysekera;Yusuke Iwasaki;T. Fujihashi;Takashi Watanabe;S. Saruwatari
本研究旨在确定无线传感获取物理空间信息能力的上限。这是一个具有挑战性的目标，因为目前，无线传感研究不断成功地获取新现象。因此，虽然我们还没有得到一个完整的答案，但在这里我们向它迈出了一步。为此，提出了一种基于生成对抗网络（GANs）的信道状态信息（CSI）到图像的转换方法CSI2Image。通过检查重建图像是否捕获了所需的物理空间信息，可以估计利用无线传感获得的物理信息的类型。我们展示了三种类型的学习方法：仅生成器学习、仅gan学习和混合学习。评估CSI2Image的性能是困难的，因为必须评估图像的清晰度和所需物理空间信息的存在。为了解决这个问题，我们提出了一种基于图像的目标检测系统的定量评估方法。采用IEEE 802.11ac压缩CSI实现了CSI2Image，评估结果表明CSI2Image成功地重建了图像。结果表明，对于简单的无线传感问题，仅使用生成器学习是足够的；然而，在复杂的无线传感问题中，gan对于重建具有更精确物理空间信息的广义图像至关重要。
This study aims to determine the upper limit of the wireless sensing capability of acquiring physical space information. This is a challenging objective because, at present, wireless sensing studies continue to succeed in acquiring novel phenomena. Thus, although we have still not obtained a complete answer, a step is taken toward it herein. To achieve this, CSI2Image, a novel channel state information (CSI)-to-image conversion method based on generative adversarial networks (GANs), is proposed. The type of physical information acquired using wireless sensing can be estimated by checking whether the reconstructed image captures the desired physical space information. We demonstrate three types of learning methods: generator-only learning, GAN-only learning, and hybrid learning. Evaluating the performance of CSI2Image is difficult because both the clarity of the image and the presence of the desired physical space information must be evaluated. To solve this problem, we propose a quantitative evaluation methodology using an image-based object detection system. CSI2Image was implemented using IEEE 802.11ac compressed CSI, and the evaluation results show that CSI2Image successfully reconstructs images. The results demonstrate that generator-only learning is sufficient for simple wireless sensing problems; however, in complex wireless sensing problems, GANs are essential for reconstructing generalized images with more accurate physical space information.