2021/05/21 by Mohsen Pourghasemian, Pourghasemian, Mohsen, Mohammad Reza Abedi +10
Computer Science · Engineering · #Distributed Control Multi-Agent Systems #FOS: Computer and information sciences #FOS: Electrical engineering #Networking and Internet Architecture (cs.NI) #Signal Processing (eess.SP) #UAV Applications and Optimization #Underwater Vehicles and Communication Systems #cs.NI #eess.SP #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2105.10282
arxiv created 2021/05/21 · openalex publication_date 2021/05/21 · arxiv updated 2021/05/24 · openalex created_date 2021/06/07 · openalex updated_date 2026/07/28
In this paper, we propose a novel joint intelligent trajectory design and resource allocation algorithm based on user's 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) to 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, placement, and scheduling the requested service's functions 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 our problem. Finally, the convergence and computational complexity of the proposed algorithm and its performance analyzed for different parameters. Simulation results show that our proposed HHCDA method decreases the request reject rate and average delay by 31.5% and 20% and increases the energy efficiency by 40% compared to DDPG method.