Research On Resource Allocation Algorithm For Space-air-ground Internet Of Remote Things Networks

Recently,with the continuously increasing requirements for wireless communication network coverage capabilities,the concept of the Internet of remote things(IoRT)has attracted widespread public attention and research.In IoRT networks of different application scenarios,smart devices are usually widely distributed in a wide,remote area that is not covered by ground access network services to monitor and sense environmental information,e.g.deserts,forests,and oceans.However,since the terrestrial communication network cannot provide services for smart devices in these application scenarios,achieving reliable uplink data transmission will face many challenges.As complement and extension of terrestrial communication networks,the satellite is an integral part of assisting IoRT networks.However,considering that smart devices are far from satellites and the path loss is large,it is not easy for smart devices to randomly access satellite networks.Meanwhile,due to the low power consumption limitations of smart devices,it is difficult to achieve long-distance and long-term transmission.In addition,the operation of satellites must take into account practical engineering challenges and costs.Therefore,unmanned aerial vehicle(UAV)is considered as an effective method to solve the above-mentioned satellite-assisted IoRT network challenges.UAV can be equipped with relay modules to amplify and forward data generated from smart devices,or it can be equipped with wireless energy transmission modules to provide energy transmission for smart devices,and they can also dynamically move to smart devices,which all help to ensure the normal operation of smart devices.Relying on such UAV-assisted space-air-ground integrated networks(SAGIN)architecture can significantly improve the energy efficiency and throughput of IoRT networks.This thesis is oriented to the remote and broad geographic environment of IoRT networks,the large number of smart devices,the large amount of data generated,low transmit power,and limited battery life.The UAV is equipped with a relay module and a wireless energy transmission module to assist smart devices to upload the generated data to the satellite,thereby forming the space-air-ground Internet of remote things(SAG-IoRT)architecture.Aiming at the rich communication resources of the SAG-IoRT network architecture,e.g.spectrum resources,power resources of different components,and space resources,this thesis focuses on the resource allocation algorithm for SAG-IoRT networks.The main work of this thesis is as follows:Firstly,for environmental monitoring and sensing scenarios in IoRT networks,this thesis proposes a joint optimization of subchannel selection,power control,and UAV deployment in static UAV-assisted SAG-IoRT networks.This resource allocation problem is a mixed integer non-convex optimization problem and cannot be solved directly.This thesis divides it into two sub-problems.In the first sub-problem,given UAV deployment scheme,the optimal sub-channel selection and power control scheme are obtained by applying Lagrangian dual decomposition.In the second sub-problem,the UAV deployment scheme based on the first sub-problem is obtained by applying successive convex approximation(SCA).Then,the two sub-problems are iterated to obtain the maximum system energy efficiency.Finally,simulations verify the convergence of the proposed resource allocation algorithm.Simulation results prove that the proposed algorithm has significant gains compared to other algorithms,and can also significantly improve system energy efficiency.Secondly,for periodic inspection scenarios in IoRT networks,this thesis proposes dynamic UAV-assisted SAG-IoRT networks to jointly optimize the throughput allocation of smart device scheduling connections,power control,and UAV trajectory.This resource allocation problem is a mixed integer non-convex optimization problem.It is difficult to obtain the optimal solution directly.It is divided into three sub-problems to deal with.In the first sub-problem,given the power control and UAV trajectory scheme,the optimal smart device scheduling connection scheme is obtained.In the second sub-problem,based on the first sub-problem scheme and the given UAV trajectory scheme,the optimal power control scheme is obtained through variable substitution and SCA.In the third sub-problem,based on the schemes obtained by the above mentioned two sub-problems,the UAV trajectory optimization scheme is obtained through variable substitution and SCA.Then,block coordinate descent(BCD)is applied to iterate the three sub-problems alternately to obtain the maximum system throughput.Finally,the simulation verifies the convergence and effectiveness of the proposed resource allocation algorithm.The simulation results prove that the proposed algorithm has obvious gains compared to other schemes,and can also increase the system throughput.Finally,for smart device business diversity and energy-constrained scenarios in IoRT networks,this thesis proposes a throughput resource allocation problem of joint UAV service power allocation ratio,power control,and UAV trajectory in dynamic UAV with energy assisted SAG-IoRT networks.This resource allocation problem is a non-convex optimization problem,which is transformed into an easy-to-handle convex optimization problem by dividing it into three sub-problems.In the first sub-problem,given the power control and UAV trajectory scheme,the optimal UAV service power allocation ratio scheme is obtained.In the second sub-problem,based on the first sub-problem scheme and the given UAV trajectory scheme,the optimal power control scheme is obtained through variable substitution and SCA.In the third sub-problem,based on the schemes obtained by the first two sub-problems,the UAV trajectory optimization scheme is obtained through variable substitution and SCA.Then,the BCD is applied to iterate the three sub-problems alternately to obtain the maximum system throughput.Finally,the simulation verifies the convergence of the proposed resource allocation algorithm.Simulation results prove that the proposed algorithm has a significant gain compared to the static UAV scheme,and can also increase the system throughput.