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

06/10/2020
by   Yu Tian, et al.
0

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.

READ FULL TEXT
research
07/31/2019

Compression and Acceleration of Neural Networks for Communications

Deep learning (DL) has achieved great success in signal processing and c...
research
04/21/2019

Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions

The new demands for high-reliability and ultra-high capacity wireless co...
research
01/11/2022

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Deep learning (DL) has proven its unprecedented success in diverse field...
research
11/10/2021

Deep Learning for Beam-Management: State-of-the-Art, Opportunities and Challenges

Benefiting from huge bandwidth resources, millimeter-wave (mmWave) commu...
research
07/12/2020

Deep Learning for Wireless Communications: An Emerging Interdisciplinary Paradigm

Wireless communications are envisioned to bring about dramatic changes i...
research
02/15/2021

Federated Dropout Learning for Hybrid Beamforming With Spatial Path Index Modulation In Multi-User mmWave-MIMO Systems

Millimeter wave multiple-input multiple-output (mmWave-MIMO) systems wit...
research
07/14/2023

From Multilayer Perceptron to GPT: A Reflection on Deep Learning Research for Wireless Physical Layer

Most research studies on deep learning (DL) applied to the physical laye...

Please sign up or login with your details

Forgot password? Click here to reset