Energy Efficient Based Resource Allocation in 5g Ultra Dense Networks Using Artificial Neural Network

Abstract

Abstract The demand for the fastest communication is a key concern for IoT technology with recent advances in the Internet of Things (IoT). With the advent of 5G telecommunications networks, the request for the quality of service (QoS) satisfaction in IoT communication can be bridged. Henceforth, a large number of devices will not be under limited resource assignment through the integration of the 5G telecommunications network. In this article, we address the above limitation on allocation by machine-learning, called the Artificial Network of prominent IoT devices (ANN). The adoption of the rules in ANN implies the allocation of resources to the most important devices and reduces them on the basis of priority. The simulation was conducted to test the effectiveness of the fuzzy system with 5G resources allocated to the IoT model. The findings indicate that the ANN model is more resource-allocating and energy efficient than other methods.