Service Stochastic Optimization with Energy Constraint Over UAV-Assisted Network

The arriving next generation network (5G) will provide people with more diverse services. However, the increasing amount of traffic has put a great deal of pressure on the network, which easily gives rise to massive network congestion. Under the context mentioned above, this paper proposes a service stochastic optimization method under the energy constraint based on the advanced unmanned aerial vehicle (UAV) technology. First, we model the service infrastructure and energy overhead as backlog queues and virtual queues, respectively. In this way, the network congestion control is transformed into a queue stability problem. Further, we consider improving the quality of experience (QoE) by optimizing the transmission rate. And the Drift-Plus-Penalty based on Lyapunov is constructed to ensure that the transmission rate of users is increased while avoiding the congestion. Finally, we carry out a succession of simulation and verify the efficiency of our proposed algorithm by fine tuning the experimental environment.