Modern technological innovations in intelligent communication systems are made possible with the most recent advances in pattern recognition and computer vision as dictated by Industry 4.0. Industry 4.0 has spread its horizons from e-management to healthcare and from agriculture to the digital economy. Hence, the application of pattern recognition and computer vision in Industry 4.0 is inevitable in the design of modern communication systems. The implementation of these techniques not only improves adaptability but also enhances the user’s potential to predict and solve unforeseen situations in a real-world scenario. Industry 4.0 relies on various communication and automation strategies.

The communication strategies are further strengthened using pattern recognition and computer vision. These technologies are supported by today’s big data era that includes both textual and visual data. The data generated from a wide variety of applications serve as the basic element for pattern recognition and computer vision algorithms for wireless communications. Hence, the requirement for multimodal data analytics derives attention from the researchers. Pattern recognition and computer vision schemes are highly resilient if a proper machine learning tool is used. Since most machine learning algorithms are available as open-source, the choice and implementation of a suitable algorithm for the wireless environment is of paramount importance.

This Special Issue focuses on the advancements in wireless communications using pattern recognition and computer vision algorithms for Industry 4.0. The ability of the machine to perform data searching, processing, and interpreting are the key aspects in the text and vision processing algorithms in Industry 4.0.

Potential topics include but are not limited to the following:

• Communication pattern recognition in autonomous systems
• Deep learning-based computer vision for wireless communications
• Mobility pattern recognition in mobile ad hoc networks
• Computer vision aided low latency communications URLL
• Machine vision communications
• Pattern recognition in cognitive communications
• Antenna selection for MIMO systems
• Channel equalization using neural networks
• Deep learning for human activity recognition in mobile computing
• Mobile computer vision
• Vision-based wireless systems
• Computer vision for indoor positioning and mobile handoff
• Deep learning-based computer vision for mmWave MIMO beamforming
• Vision-based adaptive transmission for optical wireless communication
• State-of-the-art review and developments in pattern recognition and computer vision algorithms in wireless systems.