Wireless Powered Cooperative Transmission For Low-Power Communication Networks

With the rapid development of the Internet of Things industry and wireless sensor network technology,the sustainability the sensor nodes' operation has become increasingly prominent.Due to the limited lifetime of the nodes and the inconvenient replacement,the traditional battery-based power supply method is no longer suitable for such energy-limited sensor networks.Therefore,radio frequency(RF)energy harvesting(EH)technology has emerged.Radio energy can continuously supply power to nodes in the network because of its controllability.And it also can carry energy and information at the same time.Therefore,wireless powered communication network(WPCN)has become an attractive and innovative research field.This paper mainly considers the design of information and energy transmission mechanism and resource allocation algorithm in the wireless powered communication network scenario,and optimizes the system network layout and wireless resources.And we extend the life of the network through wireless RF energy harvesting technology.Specifically,this paper studies the problem of RF energy and information transmission in a three-node multiple input single output(MISO)wireless broadcasting system.In the research,the multi-antenna transmitting node as the information source transmits information for two single-antenna receiving nodes.One of the receivers with the best of the effort quality of service(QoS)requirements has stable power supply,and provides relay and forwarding services and radio frequency energy transmission for the other passive receivers.In order to further improve the spectral efficiency of the system,this paper adopts non-orthogonal transmission mode,and in order to improve the consistency between theoretical research and practical situation,this paper uses a non-linear EH model.The innovations and main contents of this paper include the following three aspects:(1)In the single-slot MISO downlink system,a three-stage transmission protocol is designed,and an efficient resource allocation algorithm is proposed.By jointly optimizing the time fraction of each stage,the transmitter's beamforming vectors and the user's forwarding power,the throughput of system is maximized while satisfying the reliability and validity requirement of the passive user.The numerical results validate the proposed algorithm.(2)In the multi-slot MISO downlink system,a transmission strategy based on indication function is proposed,and three algorithms based on traversal search,energy threshold and sparse optimization are designed to successfully solve the multi-slot joint optimization problem.The algorithms implement business mechanism selection,beamforming design and forwarding power control.The validity and reliability of the sparse optimization algorithm are proved by experiment comparison and simulation.(3)This paper innovatively adopts machine learning method to train and learn the multi-slot joint optimization problem.By effectively adjusting the network structure and super-parameters,we train a fully connected feedforward neural network which achieving the choice of business mechanism.The simulation results show that the method has good generalization performance,and significantly improves the system operation efficiency on the premise of guaranteeing the system performance.