Energy Detection For Spectrum Sensing In Cognitive Radio

This Springer Brief focuses on the current state-of-the-art research on spectrum sensing by using energy detection, a low-complexity and low-cost technique. It includes a comprehensive summary of recent research, fundamental theories, possible architectures, useful performance measurements of energy detection and applications of energy detection. Concise, practical chapters explore conventional energy detectors, alternative forms of energy detectors, performance measurements, diversity techniques and cooperative networks. The careful analysis enables reader to identify the most efficient techniques for improving energy detection performance. Energy Detection for Spectrum Sensing in Cognitive Radio is a valuable tool for researchers and practitioners interested in spectrum sensing and cognitive radio networks. Advanced-level students studying wireless communication will also benefit from this brief.

This comprehensive reference text discusses concepts of cognitive radio and the advances in the field of spectrum sensing. This text discusses the concept of cognitive radio for next generation wireless communication and a very critical aspect of cognitive radio – that is, spectrum sensing – in detail. It covers important topics including narrowband spectrum sensing, wideband spectrum sensing, cooperative spectrum sensing, system and channel models, detection algorithms, approximation of decision statistics, and theoretical analysis of detection algorithms in detail. Separate chapters are dedicated to discussing the analysis and use of detection algorithms for narrowband spectrum sensing, wideband spectrum sensing, and cooperative wideband spectrum sensing. Aimed at graduate students and academic researchers in the fields of electrical engineering and electronics and communication engineering, this text: Discusses concepts of cognitive radio and research in spectrum sensing. Presents mathematical analysis of algorithms considering practical environment. Explains novel wideband spectrum sensing algorithms with detailed analysis. Provides mathematical derivations to help readers. Discusses basic spectrum sensing algorithms, from narrowband spectrum sensing to the more advanced wideband spectrum sensing.

In recent years, a considerable amount of effort has been devoted, both in industry and academia, towards the efficient utilization of the available spectrum under the various propagation models which lead towards the design and dimensioning of the future network Internet of Things (IoT).This book focuses on Television White Space (TVWS) opportunities and regulatory aspects for cognitive radio applications, and includes case studies for the exploitation of TVWS depending on user's mobility, and the geo-location between user and the Base Station. The book presents recent advances in spectrum sensing, reflecting state of the art technology and research achievements in this area as well as a new insights in spectrum sensing of performance modeling, analysis and worldwide applications. Technical topics discussed include: • Novel Application of TV White Space• Spectrum Sensing in Cognitive Radio• Cooperative Spectrum Sensing• DoA Estimation Algorithms

Cognitive radio networks (CRN) will be widely deployed in the near future, and this SpringerBrief covers some important aspects of it, as well as highlighting optimization strategies in Resource Allocation and Spectrum Sensing in CRNs. The cognitive approach in radio access is introduced in the first part of this SpringerBrief, and then next the benefits of cooperative spectrum sensing are highlighted and a framework for studying it under realistic channel conditions is described. New exact closed-form expressions for average false alarm probability and average detection probability are derived in this scenario. A novel approximation to alleviate the computational complexity of the proposed models are also discussed. Once the spectrum opportunities are identified, efficient and systematic resource allocation (RA) shall be performed. The second part of this SpringerBrief describes the taxonomy for the RA process in CRN. A comprehensive overview of the optimization strategies of the CRN RA is also provided. The device-to-device (D2D) communication scenario is discussed, then as a case study and various optimization strategies for the application of the CR technology in the D2D realm is studied. The application of advanced geometric water-filling (GWF) approach in CRN D2D environment for optimum resource allocation is presented in detail. Numerical results provide more insight quantitatively. Overall, this book is suitable for a wide audience that include students, faculty and researchers in wireless communication area and professionals in the wireless service industry.

Doctoral Thesis / Dissertation from the year 2020 in the subject Engineering - Communication Technology, grade: A, , language: English, abstract: Cooperative spectrum sensing technique is used to maximize the utilization of unused licensed spectrum. As the cooperation among the secondary users increases the detection performance increases, which increases the average channel throughput and energy efficiency but it depends on the number of cooperative secondary users, fusion rules, channel conditions and detection threshold. In this thesis average channel throughput, energy consumption and energy efficiency are estimated for variable number of secondary users and detection thresholds using hard fusion rules i.e. AND, OR and MAJORITY fusion rules. From the results it has been observed that the performance of AND fusion rule is better at low detection thresholds and for less number of secondary users. The performance of OR fusion rule is better at high detection thresholds and for large number of secondary users. The performance of MAJORITY fusion rule follows the performance of AND fusion rule at low detection thresholds and it follows the performance of OR fusion rule at high detection thresholds. However as the number of cooperative secondary users increases the energy required for spectrum sensing and reporting sensing results to the fusion center increases, which increases the energy consumption and reduces the energy efficiency. Therefore energy efficiency can be improved by maximizing the average channel throughput or by minimizing the energy consumption. To minimize the energy consumption in cooperative spectrum sensing, optimization technique has been proposed in this thesis and it is used for further improvement of energy efficiency. With this optimization technique, optimal number of cooperative secondary users are derived by maximizing the energy efficiency using AND and OR fusion rules but not with MAJORITY fusion rule. Because it is very difficult to estimate the optimal number of cooperative secondary users using MAJORITY fusion rule, so optimization of final decision threshold was proposed in the existing methods to maximize the energy efficiency using MAJORITY fusion rule. Therefore AND and OR fusion rules are used in this work to optimize the number of cooperative secondary users.

This book discusses the use of the spectrum sharing techniques in cognitive radio technology, in order to address the problem of spectrum scarcity for future wireless communications. The authors describe a cognitive radio medium access control (MAC) protocol, with which throughput maximization has been achieved. The discussion also includes use of this MAC protocol for imperfect sensing scenarios and its effect on the performance of cognitive radio systems. The authors also discuss how energy efficiency has been maximized in this system, by applying a simple algorithm for optimizing the transmit power of the cognitive user. The study about the channel fading in the cognitive user and licensed user and power adaption policy in this scenario under peak transmit power and interference power constraint is also present in this book.

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.