Cognitive Radio Interoperability Through Waveform Reconfiguration
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Cognitive Radio: Interoperability Through Waveform Reconfiguration
In the span of a century, radio technology advanced from spark transmitters, through analog radios based on vacuum tubes to solid state radios to finally software defined radios where most of the transmit and receive functionalities are implemented as programs running on specialized microprocessors. In recent years, cognitive radio emerged, which combines a software-defined radio with an intelligent agent, and promises to deliver a new level of functionality. This new resource addresses cognitive radio design from the perspective of interoperability with an emphasis on waveform configuration for increased flexibility and enhanced performance. The book provides readers with an extensive discussion of the concept of interoperability, as well as discusses some of the languages that could potentially be used for exchanging descriptions of waveforms.
Signal Digitization and Reconstruction in Digital Radios
This comprehensive resource provides the latest information on digitization and reconstruction (D&R) of analog signals in digital radios. Readers learn how to conduct comprehensive analysis, concisely describe the major signal processing procedures carried out in the radios, and demonstrate the dependence of these procedures on the quality of D&R. The book presents and analyzes the most promising and theoretically sound ways to improve the characteristics of D&R circuits and illustrate the influence of these improvements on the capabilities of digital radios. The book is intended to bridge the gap that exists between theorists and practical engineers developing D&R techniques by introducing new signal transmission and reception methods that can effectively utilize the unique capabilities offered by novel digitization and reconstruction techniques.
Machine Learning and Cognitive Computing for Mobile Communications and Wireless Networks
Author: Krishna Kant Singh
language: en
Publisher: John Wiley & Sons
Release Date: 2020-07-08
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.