Artificial Intelligence Assisted Wireless Resource Allocation for Wireless Network Virtualization

Abstract

Dynamic wireless resource allocation is one of the challenging problem in wireless networks to offer services with high data rate, high spectral efficiency, minimum energy and low latency when there is a high demand of limited wireless resources. To address this problem, we investigate an efficient wireless resource allocation scheme using fully connected convolutional neural network (CNN) with long time short memory (LSTM) algorithm for predicting demand of mobile virtual network operators (MVNOs) to sublease spectrum from wireless infrastructure providers (WIPs) to serve MVNOs' users. WIPs sublease wireless resources to MVNOs on the fly and MVNOs act like independent wireless service providers for their end users. While maximizing their payoffs and resource utilization, it is necessary for WIPs and MVNOs to allocate wireless resources adaptively to meet users' requirements. We present both formal mathematical analysis and numerical results to support our claims. Numerical results show that the WIP and MVNO make informed decisions based on prediction for wireless resources in order to maximum their payoffs when users change their requirements. Furthermore, performance comparison results show that the proposed approach outperforms the existing approaches in terms of payoffs, energy and delay.