Research On Key Technology Of New Generation Wireless Energy Harvesting Communication System

Nowadays,information technology has penetrated into all aspects of human production and life.The demand for a new generation of wireless networks has gradually evolved from a simple mobile phone communication network to an ubiquitous network that can provide communication between any person or object at any time and any place.In order to ensure continuous communication between nodes,energy harvesting technology is proposed as a feasible solution to extend the life of wireless networks.An important source of energy harvesting technology is to use wireless energy transmission technology(WET)to transfer energy-rich node's energy to charge low-power wireless devices by transmitting radio frequency(RF)signals.In this thesis,we proposed a long-term low-complexity online policy for energy harvesting-information sending systems to more efficiently and rationally use the energy obtained by energy harvesting.In addition,due to the existence of path fading,the traditional energy utilization efficiency is low.Therefore,we focus on the new generation of wireless communication system that uses intelligent reflective surfaces(IRS)to assist energy harvesting and information transmission,and propose the optimal algorithms for single-user and multi-user scenarios.The main contributions are as follows:First,we study the long-term online power selection strategy for energy harvestinginformation sending systems with limited battery capacity and only statistical channel state information.By using statistical channel state information and codebook design to transform codebook selection into power selection,the optimal long-term online strategies under two scenarios,Poisson arrival process and Markov arrival process,are proposed respectively.The simulation results show that our proposed algorithm is a long-term low-complexity optimal algorithm.Then,this thesis discusses the joint optimization of the IRS-assisted energy harvesting system in the case of a single receiving user.When the IRS is fixed,we use the beamforming to make the transmitter collect the most energy from energy harvesting,and use the maximum combination ratio algorithm to obtain the optimal information precoding for single-user information transmission.Next,we propose a joint optimization algorithm to obtain the optimal IRS phase matrix with the corresponding energy beamforming and information precoding.The simulation results compare the different algorithms and prove that the joint algorithm can significantly improve the system performance.Finally,further,this thesis discusses the joint optimization problem of IRS-assisted energy harvesting system in the case of multiple receiving users.The same as the single user is that the beamforming is still used for energy harveseting,but the difference is that the information precoding for multiple users cannot be directly obtained,and it needs to be solved by successive convex approximation(SCA)or other methods.Next,we propose a joint optimization algorithm that comprehensively considers energy beamforming,information precoding and IRS phase matrix to optimize the system performance.The simulation results compared with other algorithms prove that the IRS can significantly improve the system performance when there are multiple users.In summary,for energy harvesting,the IRS technology is a very promising technology,but it still needs to further explore the existing technology to effectively bring out the advantages of the IRS.