Applying Deep-Learning-Based Computer Vision to Wireless Communications: Methodologies, Opportunities, and Challenges
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Deep learning (DL) has obtained great success in computer vision (CV) field, and the related techniques have been widely used in security, healthcare, remote sensing, etc. On the other hand, visual data is universal in our daily life, which is easily generated by prevailing but low-cost cameras. Therefore, DL-based CV can be explored to obtain and forecast some useful information about the objects, e.g., the number, locations, distribution, motion, etc. Intuitively, DL-based CV can facilitate and improve the designs of wireless communications, especially in dynamic network scenarios. However, so far, it is rare to see such kind of works in the existing literature. Then, the primary purpose of this article is to introduce ideas of applying DL-based CV in wireless communications to bring some novel degrees of freedom for both theoretical researches and engineering applications. To illustrate how DL-based CV can be applied in wireless communications, an example of using DL-based CV to millimeter wave (mmWave) system is given to realize optimal mmWave multiple-input and multiple-output (MIMO) beamforming in mobile scenarios. In this example, we proposed a framework to predict the future beam indices from the previously-observed beam indices and images of street views by using ResNet, 3-dimensional ResNext, and long short term memory network. Experimental results show that our frameworks can achieve much higher accuracy than the baseline method, and visual data can help significantly improve the performance of MIMO beamforming system. Finally, we discuss the opportunities and challenges of applying DL-based CV in wireless communications.
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