Research On Joint Optimization Method Of Multidimensional Resources In Wireless Powered Communication Network

With the rapid development of technology in the first two decades of the 21 st century,wireless communication technology and Internet of Things technology have made great progress.Along with this,the surge in the number of devices and terminals in the network has created huge energy shortage in the network and has caused problems for both telecommunication operators and users.In the current communication network environment,if the low-power Io T devices and terminals are battery-powered,it will bring a lot of maintenance costs.For this,wireless power transfer is a promising solution to this problem.Wireless power transfer technology is a wireless energy transmission technology that originates from radio frequency(RF)energy harvesting technology.In the RF energy harvesting technology,the far-field radiation characteristics of electromagnetic waves can be used to convert the collected RF signal energy into electrical energy to drive wireless devices.It has the features of wide signal collection range,long distance,small power,stable and continuous energy supply.So,it is suitable for low-energy,widely distributed mobile terminal networks.This thesis investigates resource allocation in wireless powered communication networks,includes: Firstly,a joint antenna selection and resource allocation algorithm in a wireless powered communication network is proposed.The thesis considers a multi-carrier wireless powered communication network that contains a hybrid access point and multiple wireless powered users,and assumes that that the users use self-energy recovery technology.Under the transmit power constraints at the hybrid access point and energy causality constraints,an algorithm for maximizing the total user rate by jointly optimizing antenna selection,subcarrier allocations in both downlink and uplink transmissions,downlink and uplink power allocations,time allocation for the two phases and time allocation,is proposed.Simulation results show that the proposed algorithm significantly improves the total user rate compared with the reference method.Secondly,a high energy efficient joint channel and power allocation algorithm in a cognitive wireless powered communication network is proposed.The thesis considers a cognitive wireless powered communication network,which contains a cognitive base station and multiple wireless powered users who share the same spectrum with a primary user.Under the transmit power constraint at the cognitive base station and the interference power constraint of the primary user,an algorithm for maximizing the energy efficiency of the cognitive wireless powered communication network by jointly optimizing channel and power allocation is proposed based on Dinkelbach method and convex optimization method.Simulation results show that the proposed algorithm significantly improves the energy efficiency of the network compared to the reference algorithm.Lastly,a price-based energy resource allocation algorithm in a cooperative cognitive wireless powered communication networks is proposed.The thesis considers a multi-carrier cooperative cognitive wireless powered communication network,which contains a cognitive base station and multiple secondary users,sharing the spectrum with a wireless-powered primary user.Under the assumption that the primary user can harvest energy from secondary user's signals,the interaction between the primary user and the secondary user is modeled as a Stackelberg game,and an algorithm for optimizing secondary user power allocation,channel allocation,and time allocation that achieves the Stackelberg equilibrium is proposed.Simulation results show that the proposed algorithm can effectively improve the utility of the primary user and provide better communication opportunities for the secondary user compared to the reference algorithm.