Machine Learning-Based Cooperative Spectrum Sensing in A Generalized α-κ-μ Fading Channel

An improvement in spectrum usage is possible with the help of a cognitive radio network, which allows secondary users’ access to the unused licensed frequency band of a primary user. Thus, spectrum sensing is a fundamental concept in cognitive radio networks. In recent years, Cooperative spectrum sensing using machine learning has garnered a great deal of attention as a technique of enhancing sensing capability. In this study, K-means clustering is taken into consideration for the purpose of analyzing the effectiveness of cooperative spectrum sensing in a generalized α-κ-μ fading channel. The proposed approach is examined using receiver operating characteristic curves to determine its performance. The effectiveness of the proposed strategy is contrasted with that of the existing detection techniques such as Cooperating spectrum sensing based on energy detection and OR-fusion-based cooperative spectrum sensing for fading channels κ-μ, α-κ-μ. As demonstrated by results, the proposed method outshines an existing method in terms of comparison parameters, as determined by simulation results in the MATLAB version

The performance analysis of differential orthogonal space- time block codes

Ph.DDOCTOR OF PHILOSOPH

An online adaptive cooperation scheme for spectrum sensing based on a second-order statistical method

Spectrum sensing is one of the most important features of cognitive radio (CR) systems. Although spectrum sensing can be performed by a single CR, it is shown in the literature that cooperative techniques, including multiple CRs/sensors, improve the performance and reliability of spectrum sensing. Existing cooperation techniques usually assume a static communication scenario between the unknown source and sensors along with a fixed propagation environment class. In this paper, an online adaptive cooperation scheme is proposed for spectrum sensing to maintain the level of sensing reliability and performance under changing channel and environmental conditions. Each cooperating sensor analyzes second-order statistics of the received signal, which undergoes both correlated fast and slow fading. Autocorrelation estimation data from sensors are fused together by an adaptive weighted linear combination at the fusion center. Weight update operation is performed online through the use of orthogonal projection onto convex sets. Numerical results show that the performance of the proposed scheme is maintained for dynamically changing characteristics of the channel between an unknown source and sensors, even under different physical propagation environments. In addition, it is shown that the proposed cooperative scheme, which is based on second-order detectors, yields better results compared with the same fusion mechanism that is based on conventional energy detectors. © 2012 IEEE

Advanced Trends in Wireless Communications

Physical limitations on wireless communication channels impose huge challenges to reliable communication. Bandwidth limitations, propagation loss, noise and interference make the wireless channel a narrow pipe that does not readily accommodate rapid flow of data. Thus, researches aim to design systems that are suitable to operate in such channels, in order to have high performance quality of service. Also, the mobility of the communication systems requires further investigations to reduce the complexity and the power consumption of the receiver. This book aims to provide highlights of the current research in the field of wireless communications. The subjects discussed are very valuable to communication researchers rather than researchers in the wireless related areas. The book chapters cover a wide range of wireless communication topics