Research On Resource Allocation Strategy For UAV Cooperative 6G Wireless Communicatio

Real-time monitoring,emergency rescue,and smart agriculture applications etc.can benefit from unmanned aerial vehicles(UAV)and their collaboration with 6G wireless communication technology,which can enhance the network coverage,improve communication quality and reliability.UAVs carrying edge servers can extend network computing capabilities,reduce network latency,and increase network throughput.Efficient communication and data transmission can be achieved by using UAV trajectory planning and channel allocation among multiple UAVs.The related matching technique can achieve coordinated communication among users,UAVs,and satellite networks to improve communication efficiency and quality.Therefore,studying and designing reasonable resource allocation strategies for UAV collaborative 6G wireless communication networks can efficiently utilize limited resources,reduce system costs and energy consumption,and improve system efficiency and performance.Firstly,focusing on the task scheduling problem of UAV collaborative 6G wireless communication networks,the Markov Decision Process(MDP)theory is used to establish a model for task scheduling and user transmission power allocation.Based on the research on minimizing user task execution delay,the Resource Allocation Policy Gradient(RAPG)algorithm is proposed and designed to solve the target problem.Experimental results show that the(RAPG)algorithm obtains task scheduling and transmission power control strategies based on system calculation and communication resource constraints.Secondly,focusing on the UAV trajectory planning problem in 6G wireless communication,the MDP theory is used to analyze the impact of UAV communication range on user coverage and the dynamic conditions of user data upload volume changes during UAV flight.A UAV trajectory planning model is established.Based on the research on maximizing system uplink throughput,the Data Collection Trajectory Planning(DCTP)algorithm is proposed and designed to convert the interaction process between the UAV and dynamic and changing communication environments into intelligent agent decision-making processes and solve the problem.Experimental results show that the data collection trajectory planning algorithm obtains a high-throughput UAV trajectory planning strategy based on user needs and communication conditions.Finally,focusing on UAV collaborative 6G wireless communication network association matching problem,evolutionary theory and MDP theory are used to analyze resources such as user tasks,channel characteristics,UAV cache capacity,and low-orbit satellite during UAV operation.A UAV group channel allocation and association matching model is established,and the Genetic Deep Reinforcement Learning Algorithm(GD2A)is proposed and designed using genetic algorithms and deep reinforcement learning DDPG methods to solve the resource allocation problem.Experimental results show that the GD2 A algorithm obtain efficient system association matching strategies based on network resources and communication conditions in user to UAV and UAV to satellite communication.