Research On Intelligent Reflector Assisted Multilayer Heterogeneous Network

With the rapid growth of the number of mobile devices and the large-scale access of Internet of Things equipment,in order to support a large number of wireless devices with different quality of service requirements,higher requirements are also put forward for the spectrum efficiency,system capacity and transmission delay of wireless communication system.Therefore,the dense deployment of micro base stations can reduce the user ’ s wireless access distance from the spatial location,so as to improve the spectrum reuse rate,improve the system capacity and reduce the transmission delay.In multi-layer heterogeneous networks,the use of orthogonal time-frequency resources between small cells leads to spectrum efficiency and system capacity difficult to meet future mobile communication needs.Although the spectrum utilization can be improved by reusing the same frequency resources in different cell,the interference problem will be introduced.While the joint power control can reduce the interference,but also reduce the cell edge users receive signal intensity.Therefore,how to improve the spectrum efficiency in multi-layer heterogeneous networks while enhancing the received signal strength of users and reducing interference is an urgent problem to be solved.In this paper,aiming at these problems in the multi-layer heterogeneous network system,the intelligent reflection surface(IRS)technology is introduced.IRS consists of a large number of passive reflection units,each of which can introduce independent phase shift on the radio frequency(RF)signal.Through the joint control of phase shift,the reflected signal can be beam-formed,so that the reflected signal is coherently combined at the expected receiver to enhance the strength of the expected signal,or destructively combined at the unexpected receiver to suppress interference.This paper mainly studies the multi-input single-output(MISO)system with IRS assisted multi-users in multi-layer heterogeneous networks,where IRS assisted macro base stations and micro base stations provide services for multiple users.IRS is specially deployed in densely covered areas to assist wireless transmission and suppress interference.Resource optimization strategies in two scenarios are proposed for user fairness and system security.Firstly,under the power constraint of the base station and the phase shift constraint of IRS,the target of maximizing the minimum user rate is achieved by jointly optimizing the transmitting beamforming vector at the macro base station and the micro base station and the reflected beamforming vector of IRS.Since this problem is a non-convex NP-hard problem,this paper proposes an Alternating Optimization(AO)algorithm,namely,alternating optimization of the transmitting beamforming vector and the reflected beamforming vector.The transmitting beamforming vector optimization and the reflecting beamforming vector optimization are solved by the Successive Convex Approximation(SCA)and Semidefinite Relaxation(SDR)techniques in each iteration,respectively.This algorithm can obtain the local optimal rank-one solution of the optimization problem,and it can converge quickly.Finally,the simulation results show that the proposed algorithm can significantly improve the minimum rate of users and ensure the fairness of the system.Secondly,combined with physical layer security,the scenario of eavesdropper is studied.Under the power constraint of base station and the discrete phase shift constraint of IRS,the transmit beamforming vector of base station and the reflection beamforming vector of IRS are jointly optimized to ensure the safe communication of legitimate users and maximize the system throughput.Considering that the AO algorithm only optimizes some variables in each iteration,it may cause loss to the performance of the proposed scheme in maximizing the system throughput.Therefore,this paper uses a variable decoupling algorithm to solve the problem of variable coupling,and then uses SCA and SDR algorithms to jointly optimize all variables.Compared with the AO algorithm,the algorithm can theoretically make the optimization results converge to higher quality suboptimal solutions.Finally,the simulation results show that the algorithm greatly improves the throughput of the system under the condition of ensuring the safe communication of users,and is superior to the traditional AO algorithm.