Resource Allocation In Wireless Powered Communication Networks With Finite Alphabet Inputs

Energy harvesting(EH)technology is an effective way of reducing the energy consumption for the IoT terminals.Both wireless powered communication network and backscatter communication are significant applications based on RF energy harvesting technology in IoT.They can effectively improve the performance gain of wireless communication networks and realize long-term maintenance-free applications such as monitoring.With unstable energy source and limited storage device capacity,the resource allocation and scheduling strategy is important for wireless energy harvesting networks.In order to improve the spectrum efficiency of wireless communication networks,designing a reasonable resource allocation strategy is an important research direction for future works.However,a considerable performance loss usually occurs when the design based on Gaussian inputs is used for systems with practical finite alphabet inputs.Therefore,it is of great significance to explore optimization algorithms for communication systems with finite alphabet inputs.This thesis focuses on resource strategy for multiple-input multiple-output(MIMO)WPCN systems with finite alphabet inputs.The main contributions are as follows:Firstly,we study the resource allocation strategy of MIMO full duplex(FD)WPCN with finite alphabet inputs and perfect channel state information.By optimizing the precoding matrices and the time allocation vector alternately,we propose a low complexity two-step iterative algorithm to find the optimal solution.The simulation results show that compared with the Gaussian input system,the algorithm can significantly improve the performance with finite alphabet inputs.Secondly,we study the resource allocation strategy of MIMO WPCNs with finite alphabet inputs and statistical channel state information.A WPCN system for jointly wireless energy transmission(WPT)for downlink channels and information transmission with circuit loss for uplink channels is considered.Two resource allocation optimization algorithms based on improved particle swarm optimization and two-step iterative optimization algorithm are proposed to design the optimal precoding matrices and optimal time allocation vectors.At the same time,the proportional fair(PF)scheduling algorithm is proposed to analyze the system fairness.The simulation results show that the proposed PF algorithm can obtain better performance gain with finite alphabet inputs.The proposed PF algorithm can achieve higher system fairness at the cost of reducing sum-throughput of a WPCN.Finally,a resource allocation strategy of full duplex-backscatter assisted wireless powered communication network(FD-BAWPCN)with finite alphabet inputs and perfect channel state information is discussed.Sum-throughput maximization problem and working mode selection strategy in FD-BAWPCN are classified and discussed.A low complexity improved mode selection,joint precoding matrix design and time allocation optimization algorithm are proposed.A half duplex-backscatter assisted wireless powered communication network(HDBAWPCN)system is also considered.The HD-WPCN,FD-WPCN,HD-BAWPCN and FDBAWPCN systems are compared and analyzed.The numerical results demonstrate that FD system can improve the communication performance better than HD system,and BAWPCN can obtain greater performance gain.