Research On Resource Allocation Techniques For UAV Assisted Cognitive And Computation Networks

With the rapid development of the fifth-generation mobile communication technology and the Internet of Things,the number of devices accessing the network has reached an unprecedented level,bringing increasingly severe spectrum scarcity problems,and a large number of emerging compute-intensive and latency-sensitive applications have brought new challenges to the computation capacity of the network.Although cognitive radio and mobile edge computing technologies can improve spectrum efficiency of the network and provide high-performance computation services.However,traditional cognitive radio networks and mobile edge computing networks are facing severe channel fading,and the fixed base station or server deployment locations limit the scalability of the network which makes it difficult to rapidly establish communication connections in remote areas or postdisaster.Owing to the advantages of high mobility,rapid deployment and line-of-sight channels of the UAV,the UAV communication network can alleviate the severe channel fading in traditional ground communication networks and enable the rapid establishment of post-disaster emergency communication networks.The performance of the UAV-assisted communication network can be significantly improved by optimizing the trajectory of UAV.Therefore,in order to effectively improve the performance of the UAV communication network,this paper proposes an optimization framework of energy consumption in the multi-antenna UAV-assisted communication network and investigates the resource allocation and trajectory design problems in the cognitive UAV network and the UAVassisted mobile edge computing network.The main contributions are as follows.(1)To address the problem of severe channel fading in conventional cognitive radio networks,a multi-antenna UAV-assisted cognitive radio network system model is studied,in which the UAV is equipped with a cognitive base station to provide communication services for secondary users while satisfying the interference constraint of the primary user.Based on the primary user’s location information bounded error model,the robust UAV transmission energy minimization problem framework is established by jointly optimizing UAV beamforming vectors and the UAV trajectory.To solve the established nonconvex optimization problem,an iterative optimization algorithm is proposed to obtain the suboptimal solution of the original nonconvex problem based on the S-procedure theory,the successive convex approximation algorithm and the proposed rank-one theory.Simulation results show that the proposed robust resource allocation and trajectory design scheme can effectively reduce the UAV transmission energy consumption,and the UAV equipped with multiple antennas can significantly improve the network spectrum efficiency.(2)To address the problems of severe channel fading and poor network stability under the high computation task environment in the traditional mobile edge computing network,a multi-antenna relay UAV-assisted mobile edge computing network model is investigated,in which the UAV is equipped with a mobile edge server to provide computation services for users,while serving as a mobile relay to establish line-of-sight communication links between users and remote access point.By jointly optimizing users’ transmission power,users’ computation resources,UAV beamforming vectors,the on-board edge server computation resources and the UAV flight trajectory,a framework for the comprehensive energy minimization problem of the UAV-assisted edge computing network is established.An iterative optimization algorithm is developed based on the block coordinate descent algorithm and the successive convex approximation algorithm to obtain the suboptimal solution of the original nonconvex optimization problem.The closed-form expressions for the optimal user transmission power and CPU frequency are derived based on the Lagrangian dual method and the sub-gradient method,and the simulation results verify the convergence of the proposed scheme.The simulation results show that the proposed resource allocation and trajectory design scheme can significantly reduce the comprehensive energy consumption of the system,and the advantages are more significant in the environment with high incidence of computation tasks.