| To meet the requirements of extremely high transmission rate,extremely low transmission delay and extremely high transmission stability of future networks,air-ground heterogeneous networks have received extensive attention and research,becoming the important network architectures and application scenarios of current 5th generation(5G)mobile communication system and beyond 5G(B5G)era.UAV-assisted communication has gradually become an emergency communication method when ground network outage occurs,or a key technology to improve the performance of the ground network and restore users’ services when the ground network is overloaded.In the air-ground heterogeneous network,various techniques and optimization methods have emerged,promoting the continuous improvement of the air-ground heterogeneous network’s performance and user’s experience.However,with the continuous growth of the number of devices,the surge of service types,and the diversification of user service requirements,the practical deployment and application of Unmanned Aerial Vehicle(UAV)in the air-ground heterogeneous network still faces many difficulties and challenges.Ultradense network deployment,proliferating user equipment,the application of large-scale antenna arrays and millimeter-wave technology cause the more severe energy consumption in the system.In order to follow the human survival concept of sustainable development and green energy saving,the air-ground network is faced with severe challenges of energy saving.At the same time,a large number of different types of user equipment exist in the network,and cellular communication and Deviceto-Device(D2D)communication coexist.The diverse and rapidly changing service requirement makes the allocation of limited resource in the air-ground network complex and difficult to solve.Therefore,to achieve energy-saving and efficient resource allocation,we need to comprehensively consider the network topology,channel model,quality of service(QoS)and other factors in the network,comprehensively design and utilize a variety of optimization technologies to reduce network energy consumption,improve network capacity and enhance user experience.The main work and research contributions of this thesis are as follows:1.The optimization problem of relay selection,channel and power allocation for NOMA transimission in single UAV-assisted network is studied.Aiming at the problem of limited coverage and low spectral efficiency of a single UAV-assisted network,a joint relay selection,channel and power allocation algorithm for single UAV network is proposed.First,in order to expand the coverage of the UAV,an efficient energy-saving relay selection scheme is designed based on matching game.Based on the idea of reducing system power consumption,the users outside the UAV’s coverage and the users within the coverage are matched according to the level of transmission energy consumption.The designed relay selection scheme can reduce the transmission energy consumption to the largest extent on the premise of ensuring the QoS of outside users.On this basis,taking into account the different QoS requirements of the users inside the coverage area,Non-Orthogonal Multiple Access(NOMA)transmission technology is introduced to improve spectrum utilization.At the same time,,deep reinforcement learning(DRL)is used in the NOMA transmission to perform joint power and subband allocation for inside users,which reduces user transmission energy consumption and improves users’QoS compliance rate.The proposed NOMA allocation scheme can obtain optimized power selection and subband allocation strategies in different network environments.Simulation results show that the proposed joint relay selection and NOMA transmission scheme can effectively reduce energy consumption and improve users’QoS compliance rate compared with the baseline schemes.2.The optimization problem of power control and UAV clustering and in multi-UAV-assisted heterogeneous network is studied.Aiming at the serious interference problem in multi-UAV network,an interference management algorithm including power control and UAV clustering is proposed.From the perspective of maximizing the sum rate of users,the transmit power of UAVs is optimized,and the UAVs are clustered to avoid serious inter-cell interference.First,based on the interference level between the UAVs,power control is performed on UAVs using potential game.Then,a clustering algorithm based on affinity propagation is designed to reduce intra-cluster interference by means of Coordinated Multiple Points(CoMP)transmission to further improve the user rate.Simulation results verify the effectiveness of the algorithm.Compared with the non-CoMP scenario,the system sum rate is significantly improved,thus indicating that the proposed scheme has the advantages of alleviating interference and improving network performance.At the same time,the simulation results also reveal the formation law of UAV clusters,and the influence of factors such as the number of users and UAVs in the network on the clustering results and network performance.3.The joint optimization of beamwidth,power and energy harvesting time ratio in mm Wave UAV-assisted heterogeneous network is studied.Aiming at the problem of fast energy consumption and insufficient energy efficiency of D2D devices in mmWave UAV heterogeneous networks,energy harvesting technology is introduced,and a joint optimization algorithm of beamwidth,transmit power and energy harvesting time ratio of D2D users is proposed.With the goal of improving the overall energy efficiency of D2D users,alternating optimization method is adopted,in which two variables are fixed in turn,and the other variable is optimized.In each iteration,a coalition game model is first established to adjust the beamwidth of D2D users.Next,in order to eliminate the non-convexity in the power control subproblem,the original non-convex problem is transformed into convex form using the method of Dinkelbach and successive convex approximation(SCA).Finally,energy harvesting time ratio optimization is performed using linear fractional programming.Simulation results show that the proposed algorithm can achieve lower time complexity and achieve the performance close to the exhaustive search algorithm.In addition,the convergence of the proposed algorithm is also verified.4.The optimization problem of load balancing,bandwidth allocation and resource scheduling in air-ground heterogeneous networks is studied.Aiming at the problem that the terrestrial network is prone to overload with the increase of the number of users,the network service performance is degraded and user’s QoS is difficult to guarantee,the joint optimization algorithm for load balancing,bandwidth allocation and resource scheduling is designed for the heterogeneous network where Internet of Things(IoT)users and cellular users coexist.First,the heterogeneous network scenario of UAVs assisting ground base stations(BS)for data transmission is considered,and a dynamic deployment scheme of UAVBS and a load balancing algorithm are proposed to unload traffic for overloaded cells on the ground.Considering the topological structure of ground user distribution,UAV-BSs are deployed around base stations where the number of users exceeds the service capacity,and load balancing algorithm is performed according to the number of users in each cell and Reference Signal Receiving Power(RSRP)to further improve uniformity of load distribution.Then,a DRL-based dynamic bandwidth allocation and user QoS-oriented resource scheduling algorithm is designed.According to the indicators such as user QoS satisfaction,dynamic bandwidth allocation and resource scheduling strategy is performed for IoT users and cellular users.The simulation results show that the proposed algorithm can achieve more balanced user distribution and higher user QoS satisfaction compared with the benchmark scheduling schemes. |
