Rearch On The Optimization Of Transmission Strategy Aided By Intelligent Reflecting Surface In Wireless Power Transfer Networks

As the basic supporting technology of wireless energy transmission network,far-field wireless power transfer(WPT)technology can provide controllable and relatively stable power supply for terminal equipment through electromagnetic wave.However,with the rapid growth of data traffic and the massive connection of hundreds of billions of devices in recent years,the future Internet of things(IoT)has higher requirements for wireless energy transmission,including improving the transmission efficiency of end-to-end WPT and supporting non-line-of-sight(NLOS)link WPT,so as to broaden the practical application of future wireless energy transmission networks and expand the coverage of wireless energy transmission networks,and unifying wireless communication and wireless energy transmission to build a wireless powered communication network(WPCN).Recently,intelligent reflecting surface(IRS)has been proposed and applied to wireless energy transmission network as a lowcost technology to improve network performance and save energy.IRS is a passive two-dimensional reflective hypersurface,which can coordinate the reflection phase shift of a large number of passive components in IRS through intelligent software,and actively reconfigure the wireless propagation channel between transceivers to realize signal focusing and interference suppression.In addition,IRS can provide users with receiving power gain through passive beamforming,and the compatibility of IRS can also be combined with advanced communication technologies such as millimeter wave.Therefore,it has great potential for introducing IRS into the field of wireless energy transmission network.This thesis mainly studies the optimization of wireless energy transmission strategy assisted by intelligent reflector,and puts forward corresponding solutions for the two scenarios of wireless energy transmission network and wireless power supply communication network.The main work of this thesis is as follows:Firstly,for the wireless sensor network scenario with multiple sensor devices equipped with energy acquisition devices,an IRS assisted sectorized directional WPT scheme is proposed,which jointly optimizes the active beamforming at the power beacon(PB)and the passive beamforming at the IRS.The optimization problem of maximizing the total received weighted power under the constraint of energy collection of a single device is formulated.For the proposed nonconvex problem,an alternating iterative optimization algorithm is proposed.Simulation results show that,compared with the benchmark methods,IRS can effectively improve the weighted sum of power,which verifies that the combination of IRS and millimeter wave technology can further improve the system performance.Secondly,for the wireless sensor network scenario with a large number of sensor devices equipped with energy acquisition devices,a joint optimization strategy of IRS assisted uplink wireless communication and downlink wireless energy transmission is proposed.Based on the improved LEACH clustering protocol,the energy consumption and time and power resource allocation constraints of sensor nodes are presented.Under this constraint,the maximization problem of minimum system spectral efficiency is constructed by jointly optimizing the transmission power of sensor equipments,uplink and downlink transmission time,active beamforming at base station(BS)and and dynamic beamforming at IRS.In this thesis,an alternating optimization(AO)algorithm is proposed to solve the multivariable coupled nonconvex problem.The simulation results verify the feasibility of the proposed scheme,and prove that using IRS dynamic beamforming can significantly improve the spectral efficiency of WPCN systems.