Wireless Resource Allocation Based On Opportunity QoS Constraints In Dense Network

The fifth generation mobile communication technology(5G)will face the demands of higher service rate,wider coverage,and better network stability and so on.In order to meet these needs,the density of the network is getting larger and larger,and the channel interference environment is becoming more and more complex.It is almost impossible to get the channel state information(CSI)accurately.Beamforming is a key technology of 5G,which can achieve space division multiple access(SDMA),interference suppression and increasing the transmission rate.However,the traditional beamforming technology relies on the accurate CSI.In view of the unavoidable CSI error in dense network,the way of guaranteeing user's quality of service(QoS)statistically is adopted in this paper to research the resource allocation problem based on chance QoS constraint in three different network application scenarios.The specific research content and contribution include the following aspects:To minimize the total power consumption,the robust beamforming problem based on the chance signal to interference plus noise ratio(SINR)constraint is studied in dense cloud radio access network(C-RAN).First,the beamforming vectors of the network should have a sparse structure for the reason that only a few RRHs serving users in dense C-RAN network.In this paper,a RRH turn-off strategy is proposed to switch the RRH without signal transmission to sleep mode,which can significantly reduce the power consumption of the whole network.Then,the chance SINR constraint is approximated as a deterministic constraint by Bernstein inequality,and the semidefinite relaxation(SDR)technique is adopted to transform the nonconvex joint optimization problem into a convex optimization problem.Finally,an iterative re-weighted sparse beamforming algorithm is proposed and its advantages in reducing power consumption and guaranteeing the robustness of QoS are proved through simulation comparison.In the multiple input single output(MISO)network with information and energy transmission simultaneously,the beamforming based on chance energy collection constraint is studied aiming at maximizing the total user rate.First,based on the technology of simultaneous wireless information and power transfer(SWIPT),time switching signal reception mode is adopted in this paper.Then,the energy collection constraint can be approximated to deterministic convex constraint by Bernstein inequality,and an alternative optimization(AO)algorithm is proposed subsequently to decompose the original problem into two sub-problems.Because the maximization sum rate problem is nonconvex,this paper adopt minimum mean square error(MMSE)method to transform it into convex optimization problem,and use CVX convex optimization tool to solve it.Finally,the performance of the proposed algorithm in guaranteeing energy collection is proved by the simulation experiment.In multicast network scenario,a resource allocation problem based on statistical delay QoS constraint is studied in this paper to minimize the total power consumption.In this paper,the effective bandwidth and effective capacity theory are used to deal with the chance constraint problem of delay QoS,and then the original resource allocation problem is decompose into sub-channel assignment problem and power allocation problem.The Lagrange dual method is used to solve the power allocation problem,and the optimal solution of the original problem is obtained by using the dual problem.Finally,the performance of the proposed resource allocation algorithm and the algorithm in traditional unicast network are compared by simulation,which fully demonstrates the superiority of the algorithm proposed in this paper.