Название: Cognitive Radio: Computing Techniques, Network Security and Challenges
Автор: Budati Anil Kumar, Peter Ho Chiung Ching
Издательство: CRC Press
Год: 2022
Страниц: 147
Язык: английский
Формат: pdf (true)
Размер: 11.99 MB

The scarcity of radio spectrum is one of the most urgent issues at the forefront of future network research that has yet to be addressed. In order to address the problem of spectrum usage efficiency, the cognitive radio (CR) concept was proposed. The challenges of employing CR’S include that of ensuring secure device operations and data transmission with advanced computing techniques.

Machine learning (ML) is the study of computer automatic data algorithms for data analysis, which is based on sample data or training data to make predictions without being programmed. These predictions are based on computational statistics. Machining learning is the science of attainment of systems to act without being articulately programmed. It has an important role in reducing the complexity of data learning systems. In previous days, it was very difficult to handle large data analysis with human intervention, requiring commercial software or high human intervention; also sophisticated results were not obtained in the case of large data analysis. ML is one of the best methods for automatically analyzing and studying data; it is a part of Artificial intelligence (AI) to get an idea of learning from data with the help of a system for making decisions and identifying the patterns. Through this procedure, we can construct computer programs, develop suitable solutions, and improve with experience. In the past decade, ML algorithms are limited to some fixed applications such as controlling a car, web search, and controlling the understanding of the human genome. Nowadays, ML algorithms are pervasive in various fields to progress toward human-level AI. The ML algorithms can be categorized into unsupervised, supervised, and reinforcement learning methods to perform the data analysis, classification, and prediction of the information. Training sets are needed for performing the supervised classification, and the computer can select the classes in unsupervised classification. In the reinforcement learning method, a computer program interacts with a dynamic environment to perform a certain goal.

Successful development of CR systems will involve the attainment of following key objectives:

- Increasing rate and capacity for CR-based networks
- How the power is utilized in CR hardware devices with CMOS circuits
- How the Frame work is needed in complex networks
- Vedic multipliers on CR Networks
- Spatial analysis and clustering methods for traffic management
- To transmit large volume of data like video compression
- Swarm optimization algorithms
- Resource sharing in peer to peer networking

This book gathers together the latest research works focusing on the issues, challenges and solutions in the field of Cognitive Radio Networks, with various techniques. The chapters in this book will give solutions to the problems facing Industry 4.0 and will be an essential resource for the scholars in all areas of the field.