Applying Deep-Learning-Based Computer Vision to Wireless Communications: Methodologies, Opportunities, and Challenges

TL;DR

This paper explores how deep learning-based computer vision techniques can enhance wireless communication systems, particularly in dynamic scenarios, by using visual data to improve beamforming accuracy in mmWave MIMO systems.

Contribution

It introduces a novel framework combining DL-based CV with wireless communication, demonstrating improved beamforming accuracy using visual data in mobile scenarios.

Findings

Visual data significantly improves MIMO beamforming performance.

The proposed framework outperforms baseline methods in accuracy.

DL-based CV offers new opportunities for wireless communication enhancement.

Abstract

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.