A specific investigation on the Efficient resource allocation and Secure communication against Attacks in a cognitive wireless Power communication system using Multiple input and multiple output

Abstract

In the digital era of Information and Communication Technology (ICT), 5G and beyond are rapidly growing and expanding. Accordingly, many enabling technologies have received significant attention in recent years. The number of devices connected to these technologies is continually increasing. As a result, reliable and high-speed connections are required all the time and from any location. The primary goal of this research is to develop an efficient system model for resource allocation and to secure communication in the Cognitive Wireless Power Communication Network (CWPCN). In addition, Multiple-Input-Multiple-Output (MIMO) antennas are used to implement the system model. The Convolution Neural Network (CNN) techniques to optimize the system efficiency. The power adjustment process, Primary Users (PU) channel utilization, average opportunity discovery ratio of Primary and Secondary Users (SU), average capacity, and PU secrecy outage probability are investigated in this thesis. newlineThe system model is constructed using basic parameters such as PU and SU and the transmitter filter is constructed using 16-QAM modulation. Rayleigh Gaussian noise is considered for the analysis. To optimize power, a fuzzy-based clustering algorithm is used for spectrum allocation. During the design phase, a greedy optimization algorithm is used for energy harvesting. The resource allocation is based on a cooperative spectrum sensing, with an overlay model proposed here. Primary and secondary users collaborate with one another in the overlay model newline