Energy-efficient resource allocation for cognitive radio networks: a genetic algorithm approach

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 time division 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.