HYBRID SWARM OPTIMIZATION OF DEVICE-TO-DEVICE RESOURCE AND POWER ALLOCATION USING MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION
Sapana Dhan Vijay1, Bhagwat kakde2
1Department of Electronics & Communication, Madhyanchal Professional University, Bhopal, India.
2Department Electronics & Communication, Madhyanchal Professional University, Bhopal, India.
The rapid growth of users in cellular networks influences resource allocation and degrades the efficiency of cellular communication. Device-to-device communication is an emerging communication model for wireless communication without interfering with the resources of cellular networks, which provide direct communication to secondary users. Device-to-device communication explores the potential of the cellular network for emerging communication systems such as the Internet of Things and edge computing. In device-to-device communication, the major bottleneck problem is the management of resources and interference. The management of resources in device-to-device communication applied various optimization algorithms. Recently, several authors proposed swarm intelligence optimization of resources in communication systems. This paper proposes a novel resource optimization method based on particle swarm optimization. The proposed algorithm applies the multi-objective constraints function for the selection of resources in communication mode. The proposed MOPSO algorithm simulates and tests standard parameters in MATLAB environments. The performance of the proposed algorithm compares with existing resource allocation and optimization algorithms such as the genetic algorithm, ant colony optimization, and particle swarm optimization. The analysis of the results suggests that the proposed algorithm is better than existing algorithms for optimization.
Keywords: – Wireless communication, D2D, Resource allocation, Optimization, Swarm intelligence