Machine Learning and Cognitive Computing for Mobile Communications and Wireless Networks

Machine Learning and Cognitive Computing for Mobile Communications and Wireless Networks
PDF
  • eBook:
    Machine Learning and Cognitive Computing for Mobile Communications and Wireless Networks
  • Author:
    Krishna Kant Singh, Akansha Singh, Korhan Cengiz, Dac-Nhuong Le
  • Edition:
    1 edition
  • Categories:
  • Data:
    July 8, 2020
  • ISBN:
    1119640369
  • ISBN-13:
    9781119640363
  • Language:
    English
  • Pages:
    272 pages
  • Format:
    PDF

Book Description
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.

Content

1. Machine Learning Architecture and Framework
2. Cognitive Computing: Architecture, Technologies and Intelligent Applications
3. Deep Reinforcement Learning for Wireless Network
4. Cognitive Computing for Smart Communication
5. Spectrum Sensing and Allocation Schemes for Cognitive Radio
6. Significance of Wireless Technology in Internet of Things (IoT)
7. Architectures and Protocols for Next-Generation Cognitive Networking
8. Analysis of Peak-to-Average Power Ratio in OFDM Systems Using Cognitive Radio Technology
9. A Threshold-Based Optimization Energy-Efficient Routing Technique in Heterogeneous Wireless Sensor Networks
10. Efficacy of Big Data Application in Smart Cities

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