AI-Based Mobility-Aware Energy Efficient Resource Allocation and Trajectory Design for NFV Enabled Aerial Networks

In this paper, we propose a novel joint intelligent trajectory design and resource allocation algorithm based on users' mobility and their requested services for unmanned aerial vehicles (UAVs) assisted networks, where UAVs act as nodes of a network function virtualization (NFV) enabled network. Our objective is to maximize energy efficiency and minimize the average delay on all services by allocating the limited radio and NFV resources. In addition, due to the traffic conditions and mobility of users, we let some virtual network functions (VNFs) migrate from their current locations to other locations to satisfy the Quality of Service requirements. We formulate our problem to find near-optimal locations of UAVs, transmit power, subcarrier assignment, VNF placement, and VNF scheduling over the UAVs and perform suitable VNF migration. Then we propose a novel hierarchical hybrid continuous and discrete action (HHCDA) deep reinforcement learning method to solve the proposed problem. Finally, the convergence and computational complexity of the proposed algorithm and its performance is analyzed for different parameters. Simulation results show that our proposed HHCDA method decreases the average end-to-end delay by 31.5% and increases the energy efficiency by 40% compared to the state-of-the-art Deep Deterministic Policy Gradient method.