Energy-Efficient Resource Allocation for Cognitive Radio Networks: A Genetic Algorithm Approach
Zaied, Y., Shokair, M., Abdelatty, S., Saad, W. (2024). Energy-Efficient Resource Allocation for Cognitive Radio Networks: A Genetic Algorithm Approach. EKB Journal Management System, 33(2), 32-39. doi: 10.21608/mjeer.2024.251264.1087
Yasmeen A Zaied; Mona Shokair; Saed Abdelatty; Waleed Saad. "Energy-Efficient Resource Allocation for Cognitive Radio Networks: A Genetic Algorithm Approach". EKB Journal Management System, 33, 2, 2024, 32-39. doi: 10.21608/mjeer.2024.251264.1087
Zaied, Y., Shokair, M., Abdelatty, S., Saad, W. (2024). 'Energy-Efficient Resource Allocation for Cognitive Radio Networks: A Genetic Algorithm Approach', EKB Journal Management System, 33(2), pp. 32-39. doi: 10.21608/mjeer.2024.251264.1087
Zaied, Y., Shokair, M., Abdelatty, S., Saad, W. Energy-Efficient Resource Allocation for Cognitive Radio Networks: A Genetic Algorithm Approach. EKB Journal Management System, 2024; 33(2): 32-39. doi: 10.21608/mjeer.2024.251264.1087

Energy-Efficient Resource Allocation for Cognitive Radio Networks: A Genetic Algorithm Approach

Menoufia Journal of Electronic Engineering Research
Volume 33, Issue 2, July 2024, Page 32-39  XML PDF (399.03 K)
Document Type: Original Article
DOI: 10.21608/mjeer.2024.251264.1087
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Authors
Yasmeen A Zaied email 1; Mona Shokair2; Saed Abdelatty1; Waleed Saad3
1Faculty of Electronic Eng., Menoufia University
2Faculty of Electronic Engineering, Menoufia University
3Faculty of Engineering, Shaqra University, Dawadmi, AL Riyadh, Saudi Arabia. Faculty of Electronic Engineering, Menoufia University.
Abstract
Abstract—Cognitive radio networks, where secondary users
opportunistically share spectrum resources with prime users to
improve spectrum utilization, energy-efficient resource allocation is a critical concern. In order to solve the optimization problem of optimizing network lifetime while satisfying energy limitations for both primary and secondary users, a genetic algorithm-based method is presented in this paper. The network consists of a timedivision multiple access (TDMA) frame with a variable number of time slots, a primary user base station, a secondary user base station, primary users, and secondary users. The effectiveness of the genetic algorithm in identifying solutions that strike a balance between energy consumption and energy harvesting, improving network lifetime, is demonstrated by simulation results. Additionally, the study explores the effects of altering the number of primary and secondary users, as well as time slots, on the optimization process. The paper uses a genetic algorithm-based strategy to solve the optimization problem of maximizing network lifetime while satisfying energy limits for both primary and secondary users.
Keywords
Wireless Sensor Networks (WSN); Cognitive radio routing; energy harvesting; wireless-powered communication network
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