Resource Allocation Research On Wireless Powered Communication Networks

Along with the rapid development of wireless communication networks and the increasing number of terminals in access networks,Internet of Things(IoT)devices will widely exist in future wireless communication systems.How to prolong the lifetime of these ubiquitous devices is an urgent problem to be solved for IoT.Wireless Powered Communication Networks(WPCN)can use radio frequency energy signals to transmit energy to the battery-free IoT terminals,which is an essential way to solve the energy issues of devices in the IoT,and has attracted wide attention from both academia and industry.However,WPCN will be affected by the dual path loss in the energy and information transfer phase,which greatly reduces the transmission distance and coverage of WPCN.Therefore,it is of great significance to study the efficient resource allocation method of the WPCN to improve the transmission capacity and energy efficiency,and to further enhance the lifetime of WPCN.This paper summarizes the current research status of wireless energy transfer,and on this basis,forms the overall research ideas of this paper:aiming at the maximization of the capacity,energy efficiency and utilization efficiency of green energy,this paper optimizes the available resources of WPCN and reduce the impact of the dual path loss in WPCN,so as to obtain higher transmission rate and lifetime of the network.The specific research contents of this paper include the following three aspects.1.The sum-rate maximization resource allocation of the multi-antenna battery-free relay-assisted WPCN is studied in this paper,and the sum-rate maximization algorithms by jointly optimizing the energy transmission beamforming matrix and time duration allocation are proposed.For the TDMA scenarios,the optimal algorithm is proposed by introducing the slack variables.To reduce the high computation complexity of the optimal algorithm because of the matrix variable,the closed expression of optimal time duration allocation for multiple terminals accessing the relay is derived,the semi-closed expression of the optimal energy beaforming matrix with given energy transmission and relay forwarding time is further derived,and a joint optimization algorithm of energy transmission time and beamforming based on iterative optimization is further proposed.For the SDMA scenarios,the semi-closed expression of the optimal energy beamforming matrix of the energy transmitting node is deduced when the time division is given.The optimal time duration allocation method of the given energy beamforming matrix is proposed,and a joint optimization algorithm of energy transmission time and beamforming based on this method is proposed to maximize the sum-rate of WPCN.Compared with the existing research,the introduction of multi-antenna passive relay in both time division multiplexing scenarios and space division multiplexing scenarios can significantly improve the sum-rate of WPCN,and the proposed suboptimal deliver a low performance loss compared with the optimal algorithm.2.The energy efficiency maximization problem of the traditional energy powered WPCN is studied.In this paper,the energy efficiency algorithm by jointly optimizing the time duration and transmission power is proposed for both amplify-and-forwarding(AF)relay and decode-and-forward(DF)relay powered WPCN.Specifically,for the AF relay powered WPCN,the closed form of the optimal information transfer time duration from terminal to the relay is derived.Based on the closed expression,an optimal parameter selection algorithm is designed,and a joint optimization strategy for the time duration and power allocation is designed.The simulation results verify the effectiveness of the proposed strategy,and illustrate that the performance of the joint optimization strategy of time division and power allocation is superior to existing strategies.For the DF relay powered WPCN,the optimal time duration allocation and the closed expression of transmit power in the energy transmission phase of relay nodes are derived,respectively,and an optimal joint optimization algorithm of power allocation and time duration allocation is designed.The simulation results verify the superiority of the proposed strategy.3.The harvested energy efficient resource allocation of the green energy powered WPCN is studied.The optimal offline energy management strategy and an online energy flow algorithm based on sliding window are proposed for the multi-slot green energy powered WPCN.For the geographical differences of the green energy arrival,an algorithm by jointly optimize the wired energy cooperation between different energy nodes,wireless beamforming and time duration allocation is proposed to maximize green energy utilizing efficiency.Specifically,this paper first studies the time management strategy of green energy in WPCN,derives the optimal closed-form solution of information transmission time,proposes an optimal energy allocation algorithm with energy constraints,and designs the optimal offline directional energy flow strategy.Considering the non-causality of energy arrival information and channel information,an online energy management strategy based on sliding window is proposed.Compared with greedy energy utilization,the proposed offline energy management strategy and online energy management strategy significantly improve the multi-slot sum-rate of WPCN.Furthermore,considering that the WPCN may have multiple energy harvesting nodes,this paper establishes the joint energy wired and wireless cooperation model between multiple energy nodes,derives the closed-form solution of the optimal information transmission time and energy beamforming matrix,and proposes a joint optimization algorithm for energy transmission time and wired cooperation,so as to design the joint time duration allocation and energy flow optimization algorithm.Compared with the existing algorithms,the proposed algorithm matches the unevenness of energy arrival and the difference of channel state between different nodes,which significantly improves the energy utilization efficiency.