Performance Enhancement For Wireless Powered Backscatter Communication Network

With the development of 5G/B5 G technology,Internet of Things(IoT)has developed vigorously.For the IoT applications,wireless devices are deployed ubiquitously,which are usually powered by traditional batteries.However,to meet the requirements of future communications,the traditional power supply method based on batteries has some disadvantages,e.g.,limited life and inconvenient replacement.Wireless powered backscatter communication network(WPBCN)makes full use of the advantages of harvest-then-transmit(HTT)mode and backscatter communication(BackCom)mode to construct an active-passive communication fusion mechanism,which can efficiently address the issue of power supply.However,the efficiency of energy harvesting,information backscattering and information transmission in the WPBCN is unsatisfied.In addition,solutions to model the energy interaction between devices from different service providers and improve the time scheduling efficiency are also urgent needs.To address the above problems,this thesis studies the performance optimization scheme of the WPBCN by using new technologies and new methods.The main works and contributions are summarized as follows:In order to improve the transmission efficiency of the cognitive radio(CR)empowered WPBCN,an intelligent reflecting surface(IRS)enabled transmission scheme is proposed for the Overlay scenario.In particular,the secondary transmitters(STs)can work in either BackCom mode or HTT mode.The IRS is deployed to assist in the energy harvesting,information backscattering and information transmission.In the busy phase of the primary transmitter(PT),the IRS is used to improve the efficiency of information backscattering and the amount of harvested energy of the STs at the same time.In the idle phase of the PT,the IRS is used to improve the information transmission efficiency of the STs.Considering the piece-wise linear energy harvesting model,an efficient time scheduling scheme is designed in the busy phase.Specifically,when an ST backscatters information in a time slot,the rest of the STs can simultaneously harvest energy from the PT.To maximize the system sum-rate,an optimization problem about the phase shifts at the IRS,time scheduling,and power allocation is formulated.Since the formulated problem is non-convex,an alternating optimization algorithm is proposed to obtain a sub-optimal solution.In particular,the optimal IRS phase shifts in the idle phase are first derived in the closed-form.Then,the variable substitution techniques are used to solve the sub-problem of time scheduling and power allocation.Finally,for the sub-problem of optimizing the IRS phase shifts in the busy phase,the Taylor approximation and semi-definite relaxation(SDR)are applied to transform it into a convex optimization problem,and the standard convex optimization technique is used to solve it.Simulation results confirm that the proposed scheme can achieve up to 333.3% sum-rate gain over the benchmark schemes.The above work is based on the assumption that the energy supply is free.However,the power beacon(PB)and wireless devices may belong to different service providers,both of which aim to maximize their respective benefits.We consider the scenario that there exist two providers in the WPBCN,i.e.,the energy service provider and IoT service provider.Specifically,the PB is from the energy service provider,which is considered as the follower.The IoT service provider,including wireless devices and the access point(AP),is considered as the leader.Based on the above settings,an energy interaction model based on Stackelberg game is proposed.Specifically,the follower supplies energy for the leader,and the leader has to pay for its received energy service.Moreover,an efficient time scheduling scheme for network performance enhancement is proposed,based on which each wireless device can harvest energy over the entire block except its time slots allocated for information backscattering and information transmission.Considering the non-linear energy harvesting model,the optimization problems are formulated to maximize the utility values of the leader and the follower,respectively.To address the non-convexity of the leader-level problem,the original problem is decomposed into two sub-problems,which are iteratively solved in an alternating manner.Specifically,the Taylor approximation,SDR and variable substitution techniques are applied to find a sub-optimal solution.To evaluate the performance loss caused by the interaction between two providers,the social welfare maximization problem is further investigated.Numerical results demonstrate that,compared with BackCom scheme,HTT scheme and equal time scheme,the proposed scheme can achieve up to 35.4% and 38.7% utility gain for the leader and the follower,respectively.