Throughput Optimization For Reconfigurable Intelligent Surface-assisted Wireless Powered Communication Networks

As more and more devices are added to the Internet of Things(Io T),the energy constraints of the devices become one of the obstacles to the development of the Io T.The proposed wireless power communication networks(WPCNs)can effectively solve the energyshortage problem of devices.However,for large-scale industrial Io T where the number of communication devices is huge,the communication performance of a WPCN is limited due to the uncontrollable environment.Reconfigurable intelligent surface(RIS)has been proposed as a promising technology for reconfiguring the wireless propagation environment by utilizing highly reflective beams to achieve an effective increase in signal coverage and energy efficiency of the communication system.Previous research works have confirmed that the information transmission throughput of WPCN can be improved with the aid of the RIS,but the challenging problem of how to design the user information transmission method and optimize the reflection phase of the RIS to achieve a balance of complexity and performance remains.This thesis investigates the problem of maximizing the throughput of a RIS-assisted WPCN by optimizing the reflected phase of the RIS and the resource allocation of the system to maximize the throughput of the network and focuses on the following two aspects.First,this thesis considers a RIS-assisted WPCNs,where a base station(BS)transmits energy to multiple users grouped into multiple clusters in the downlink,and the clustered users transmit information to the BS in the manner of hybrid non-orthogonal multiple access and time division multiple access in the uplink.We investigate optimizing the reflect beamforming of the IRS and the time allocation among the BS's power transfer and different user clusters' information transmission to maximize the throughput of the network.Since the objective function is not a concave function with respect to the optimization variables and the constraints include a non-convex modulo-1 function,the considered problem is a non-convex optimization problem.This thesis proposes an efficient algorithm based on the block coordinate ascent(BCA),semidefinite relaxation(SDR),and sequential rank-one constraint relaxation techniques to solve the resultant problem.The simulation results show that compared with other benchmark schemes,optimizing the reflection phase of RIS can greatly improve the throughput of wpcn information transmission,and show the impact of user clustering settings on the network throughput performance.Second,this thesis further investigates a sustainablely working RIS-assisted WPCN.In this network,unlike the previous one that does not consider the energy consumption impact of the RIS,the RIS needs to acquire energy to sustain the subsequent signal reflection work.To achieve the sustainable working of the system,this thesis considers an energy distribution at the RIS,where a part of the energy is collected by itself to ensure the working energy demand and another part is reflected to the user,using a realistic non-linear model to model all energy collection processes.To maximize the throughput of user uplink information transmission,the reflected phase and energy allocation coefficients of the RIS,the transmit beamforming of the base station,and the time allocation of downlink energy transmission and uplink information transmission are jointly optimized.We use the BCA and SDR techniques to solve the original problem in a relaxed form and use Gaussian randomization optimization to obtain a suboptimal solution.Simulation results show that the proposed algorithm can significantly improve the system throughput compared to existing benchmark schemes.