Perfomance Comparison of Genetic and Greedy Algorithms in Underlay Device-to-Device Communication

• Main Authors: Salma Pratiwi, Arfianto Fahmi, Vinsensius Sigit Widhi Prabowo
• Format: Article
• Language: English
• Published: Politeknik Elektronika Negeri Surabaya 2020-12-01
• Series: Emitter: International Journal of Engineering Technology
• Subjects: Device-to-Device (D2D); Genetic Algorithm; Spectral Efficiency; Energy Efficiency; Interference Mitigation
• Online Access: https://emitter.pens.ac.id/index.php/emitter/article/view/566

The number of cellular users (CU) continues to increase in Indonesia. This impacts a large network load for the number of devices connected to the main network so it will have an impact on the quality of service. Device-to-Device (D2D) communication as components for LTE-A technology enabling a direct wireless link between the CUs without routing the data via the evolved Node B (eNB) signal or the core network. The need for algorithm and power control used to allocate radio resources so it can get a good quality of service because of communications technology D2D. In this study, we analyze and compare the performance parameters of D2D communication systems, including system interference, system sum-rate, system spectral efficiency, total energy system, and system energy efficiency based on Genetic and Greedy Algorithms in allocating radio resources and controlling the power of users. The genetic algorithm works with three operators in allocating resource block (RB), including proportional selection, crossover, and mutation. This process is repeated many times to produce several generations so that the best allocation can be got. The genetic algorithm has a flexible number of D2D and cellular communications in several RBs, minimum signal to interference plus noise ratio (SINR) also considered for mobile communication in ensuring the quality of its services. Numerical evaluations demonstrate the superior performance of the Genetic Algorithm in terms of system power, energy efficiency, and interference mitigation. As repetition gets larger, the Genetic algorithm results in better spectral efficiency.