A Review of Efficient Resource Allocation for mm-Wave D2D Communication in Cellular Networks using ML Algorithm

Recently, mobile data traffic is rapidly increasing due to large data transmission in the cellular network. Researchers have proposed several techniques to handle this situation and increase the network capacity of the system. However, data path loss, unexpected blockage in the network, and other features make the network implementation of mm-Wave bands more challenging. Therefore, resource allocation is considered as a significant task to reduce the mutual interference, transmission delay, and offload data traffic from the base station. In this research study, the effective resource allocation techniques for D2D in mm-Wave communication are analyzed using Machine Learning (ML) based algorithms. This study develops a unique framework for deploying and operating D2D mm-Wave communication networks in wireless cellular systems, UAV networks, and wearable device networks by utilizing advanced mathematical techniques from ML. The network throughput, energy efficiency, and convergence rate are considered as key parameters for defining the effectiveness of resource allocation. This comprehensive research study supports the researchers to obtain the best solution for the current issues in D2D mm-Wave communication in a cellular network.