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In this paper, we propose a novel joint intelligent trajectory design andresource allocation algorithm based on user's mobility and their requestedservices for unmanned aerial vehicles (UAVs) assisted networks, where UAVs actas nodes of a network function virtualization (NFV) enabled network. Ourobjective is to maximize energy efficiency and minimize the average delay onall services by allocating the limited radio and NFV resources. In addition,due to the traffic conditions and mobility of users, we let some VirtualNetwork Functions (VNFs) to migrate from their current locations to otherlocations to satisfy the Quality of Service requirements. We formulate ourproblem to find near-optimal locations of UAVs, transmit power, subcarrierassignment, placement, and scheduling the requested service's functions overthe UAVs and perform suitable VNF migration. Then we propose a novelHierarchical Hybrid Continuous and Discrete Action (HHCDA) deep reinforcementlearning method to solve our problem. Finally, the convergence andcomputational complexity of the proposed algorithm and its performance analyzedfor different parameters. Simulation results show that our proposed HHCDAmethod decreases the request reject rate and average delay by 31.5% and 20% andincreases the energy efficiency by 40% compared to DDPG method.

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