Research And Verification Of Resource Allocation For UAV Aided D2D Communication

In the face of communication scenarios such as the damage of base stations in disaster areas or the loss of isolated island base stations in remote mountainous areas,traditional cellular wireless communication can not quickly solve the problems of communication recovery and power interruption in time.Therefore,it is significant to establish an emergency communication network for rapid and efficient deployment of airbase stations(ABS).Compared with the traditional ground cellular base station,the unmanned aerial vehicles(UAV)are flexible,maneuverable,rapid real-time deployment,small volume,low cost and so on.At the same time,device-to-device(D2D)communication technology in the cellular network effectively improves the utilization of spectrum resources and alleviates the shortage of spectrum resources by multiplexing the cellular spectrum.In this context,a UAV assisted D2 D communication model is proposed to realize the rapid deployment of ABS and more efficient utilization of limited spectrum in the emergency communication scenario,and effectively solve the problems of untimely post-disaster communication recovery and sharp increase of communication traffic.The specific work and innovations are as follows:Firstly,aiming at the power limitation and spectrum shortage of UAV emergency communication network,a UAV-assisted D2 D communication model is proposed.Taking UAV as ABS to provide communication services for ground users,it can be deployed quickly and flexibly and work stably while realizing signal coverage;The D2 D communication is introduced into the UAV assisted wireless communication network to improve the energy efficiency of D2 D users in the system while controlling interference.Aiming at the problem of rapid deployment of UAVs in UAV assisted D2 D communication system,a deployment strategy of minimum number and optimal location of UAVs based on particle swarm optimization(PSO)-Kmeans clustering algorithm is proposed to minimize the number of UAVs in the system and optimize the location of UAVs.Finally,the validity of the algorithm is verified by simulation.The simulation results show that under the same user distribution constraints,compared with the PSO algorithm,PSO-Kmeans can increase the minimum SINR of CUEs by50.7% on average and ensure the communication quality of CUEs.Secondly,aiming at the low energy efficiency of D2 D users in the research of D2 D resource allocation,a D2 D channel allocation and power control strategy based on quantity weight adaptive salp swarm algorithm(QWASSA)and adaptive mutation SALP swarm algorithm(AMSSA)is proposed.Through modeling,the joint channel allocation and power control scheme is obtained to maximize the energy efficiency of D2 D users.Then,QWASSA is proposed.By introducing the change strategy of leaderfollower number,QWASSA is not easy to fall into local optimization,and the local mining ability is further improved.Then,AMSSA is proposed based on QWASSA,introducing the leader-follower Coleoptera adaptive adjustment strategy to update the position of the mutated Coleoptera.At the same time,considering the Q-learning,the reward is given according to whether the individual’s following behavior can improve the fitness,so that the individual can obtain the ability of independent following,choose the follower independently,and avoid falling into local optimization due to blind following,energy efficiency of D2 D users is further improved.Finally,the effectiveness of the algorithm is verified by simulation.The results show that under the same user distribution and UAV deployment constraints,QWASSSA and MSSA improve the energy efficiency of D2 D users by 11% and 16.5% respectively compared with the PSO algorithm;Compared with the bottle sea squirt algorithm,the energy efficiency of D2 D users is improved by 4.2% and 9.1% respectively.Thirdly,in order to make the UAV auxiliary D2 D communication system intuitive and vivid display,the verification platform of the UAV aided D2 D communication system is built by using Matlab GUI.The deployment,construction process and parameter setting of each module are described in detail.The difficulty of platform construction lies in the connection between each module control and function realization program,the transmission of parameters and the transmission and display of program operation results.Finally,to ensure the accuracy of the platform display results,the module function is tested.The test results show that the system can complete the functions of cues and D2 D user deployment,UAV deployment and 3D position display,calculation of SINR of cues,algorithm selection of D2 D user energy efficiency optimization and operation curve display according to the input data.