Enhanced Resource Optimization Methods for Device-To-Device (D2D) Communication in Next Generation Wireless Networks

The increasing number of users in cellular networks affects resource allocation and lowers communication efficiency. Device-to-device communication is a new approach that allows wireless devices to communicate directly without using cellular network resources, benefiting emerging systems like the Internet of Things and edge computing. However, a key challenge is managing resources and interference in this type of communication. Various optimization techniques have been employed for this purpose. Recently, researchers have focused on swarm intelligence for resource optimization in communication systems. This paper presents a new method for resource optimization using particle swarm optimization (PSO). The innovative algorithm employs multi-objective constraint functions to select communication resources. Tested and simulated in MATLAB environments, the MOPSO algorithm's performance is compared with other algorithms like genetic algorithms, ant colony optimization, and particle swarm optimization. The results show that the proposed algorithm performs better than existing optimization methods. Keywords- D2D Communication, Resource Optimization, Machine Learning, Swarm Intelligence, Wireless Networks, Interference Management, Spectrum Efficiency.