论文标题
将基于深度学习的计算机视觉应用于无线通信：方法，机会和挑战
Applying Deep-Learning-Based Computer Vision to Wireless Communications: Methodologies, Opportunities, and Challenges
论文作者
论文摘要
深度学习（DL）在计算机视觉（CV）领域取得了巨大的成功，并且相关技术已用于安全，医疗保健，遥感和许多其他领域。作为平行的发展，视觉数据在日常生活中变得普遍，无处不在的低成本摄像机很容易产生。因此，探索基于DL的简历可能会产生有关对象的有用信息，例如其数量，位置，分布，运动等。直观地，基于DL的CV也可以促进和改善无线通信的设计，尤其是在动态网络方案中。但是，到目前为止，这种工作在文献中很少见。因此，本文的主要目的是介绍有关将基于DL的简历应用于无线通信的想法，以将一些新颖的自由度带到理论研究和工程应用中。为了说明如何在无线通信中应用基于DL的CV，使用基于DL的CV与毫米波（MMWave）系统使用的示例可以在移动方案中实现最佳的MMMWAVE多输入和多输出（MIMO）光束成形。在此示例中，我们提出了一个框架，以使用Resnet，3维回复和一个长的短期记忆网络来预测先前观察到的梁索引和街道视图的图像的未来光束指标。实验结果表明，我们的框架的精度比基线方法高得多，并且视觉数据可以显着改善MIMO波束形成系统的性能。最后，我们讨论在无线通信中应用基于DL的简历的机会和挑战。
Deep learning (DL) has seen great success in the computer vision (CV) field, and related techniques have been used in security, healthcare, remote sensing, and many other fields. As a parallel development, visual data has become universal in daily life, easily generated by ubiquitous low-cost cameras. Therefore, exploring DL-based CV may yield useful information about objects, such as their number, locations, distribution, motion, etc. Intuitively, DL-based CV can also facilitate and improve the designs of wireless communications, especially in dynamic network scenarios. However, so far, such work is rare in the literature. The primary purpose of this article, then, is to introduce ideas about applying DL-based CV in wireless communications to bring some novel degrees of freedom to both theoretical research and engineering applications. To illustrate how DL-based CV can be applied in wireless communications, an example of using a DL-based CV with a millimeter-wave (mmWave) system is given to realize optimal mmWave multiple-input and multiple-output (MIMO) beamforming in mobile scenarios. In this example, we propose a framework to predict future beam indices from previously observed beam indices and images of street views using ResNet, 3-dimensional ResNext, and a long short-term memory network. The experimental results show that our frameworks achieve much higher accuracy than the baseline method, and that visual data can significantly improve the performance of the MIMO beamforming system. Finally, we discuss the opportunities and challenges of applying DL-based CV in wireless communications.
