Research On Bandwidth Allocation And Trajectory Optimization Methods In UAV-assisted Communication Networks

Because UAVs have the advantages of flexible deployment,fast response,strong environmental adaptability,and high probability of air-ground line-of-sight transmission,UAV-based wireless communication has broad application prospects.With the vigorous development of the fifth-generation mobile communication technology(5G),the integration of intelligent application scenarios and UAV-based mobile communication technology will reach a new height.As hotspot areas need to increase network coverage and system capacity in a short period of time,the use of drones for auxiliary communication becomes feasible.As a mobile base station or relay in the air,the UAV will provide fast communication offloading to achieve the purpose of improving the throughput of the communication system.For emergency communication scenarios,UAVs can be quickly deployed to provide emergency visual link communication at low altitudes in the area when the communication infrastructure is unavailable.This paper mainly studies the UAV edge unloading model and the post-disaster UAV rapid deployment scenario.Firstly,in the model of UAV-assisted communication and mobile edge computing,a non-convex optimization problem with maximizing UAV energy efficiency as the objective function is established;then,in the scenario of UAV post-disaster emergency communication deployment,the shortest Path planning and energy consumption minimization problems.The specific work is as follows:(1)As a mobile base station in the air,the unmanned aerial vehicle under the condition that the UAV performs communication offloading at the edge of the cellular system and serves the ground mobile devices in the cellular network together with the ground base station,while causing communication interference to the area served by the ground base station,is studied.Human-machine energy efficiency maximization model.In the process of establishing the optimization model,it is necessary to consider the flight speed of the optimized UAV,the geographical division of the mobile devices served by the UAV and the mobile devices served by the ground base station,and the bandwidth allocation ratio between the UAV and the ground base station,etc.constraint.Since the original objective function is a non-convex optimization problem,the method of decomposing the original function into two independent sub-functions is chosen to convert the non-convex optimization problem into a convex optimization problem.Using a reasonable optimization method,the optimal UAV flight speed and optimal bandwidth allocation ratio that maximizes the energy efficiency of the UAV under the condition of a certain area division are obtained,and the iterative optimization method is used to solve the optimal constraint condition.to maximize the energy efficiency of the UAV.The simulation results show that the energy efficiency maximization proposed in this paper can be achieved under the conditions of satisfying the optimal UAV flight speed,service area division and bandwidth allocation strategy.(2)Research on the shortest path planning and energy consumption minimization model for the UAV as a mobile base station in the air in the case of emergency communication.In this paper,the optimal communication points that the drone will head to and hover over to provide communication services have been monitored in advance.In order to make the model more suitable for the real situation,the communication points are randomly and uniformly distributed in the area each time.Considering whether the flight starting point of the UAV is fixed and whether it returns to the starting point after each communication service,this paper uses two UAV shortest path planning algorithms.In the UAV shortest path planning based on genetic algorithm,the first communication point randomly generated is selected as the starting point of the UAV to start the communication service of the shortest path,and the last communication point reached is used as the starting point of the next flight.starting point.In the UAV shortest path planning based on the ant colony algorithm,a point is randomly determined as the starting point of the UAV’s shortest path each time,and it will return to this starting point immediately after serving the last communication point.The above two shortest path planning algorithms are compared with the UAV that randomly selects a path for communication services.The UAV based on the shortest path planning proposed in this paper has significantly lower energy consumption.