Machine Learning and Cognitive Computing for Mobile Communications and Wireless Networks

The basic architecture of a cognitive computing framework is shown in Figure 4.3.
The bottom layer shows the basic infrastructure like 5G networks [6, 7], deep
learning and AI, big data and IoT services which are used to design the
framework ...

Machine Learning and Cognitive Computing for Mobile Communications and Wireless Networks

Machine Learning and Cognitive Computing for Mobile Communications and Wireless Networks

Communication and network technology has witnessed recent rapid development and numerous information services and applications have been developed globally. These technologies have high impact on society and the way people are leading their lives. The advancement in technology has undoubtedly improved the quality of service and user experience yet a lot needs to be still done. Some areas that still need improvement include seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability and so on. Thus, it is highly desirable to develop smart technologies for communication to improve the overall services and management of wireless communication. Machine learning and cognitive computing have converged to give some groundbreaking solutions for smart machines. With these two technologies coming together, the machines can acquire the ability to reason similar to the human brain. The research area of machine learning and cognitive computing cover many fields like psychology, biology, signal processing, physics, information theory, mathematics, and statistics that can be used effectively for topology management. Therefore, the utilization of machine learning techniques like data analytics and cognitive power will lead to better performance of communication and wireless systems.

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