Research On Resource Optimization For Massive MIMO Comunication Systems In 5G

With the rapid development of mobile communication technology,the explosively increasing network terminal devices and Resource allocation requirements between them are bringing great challenges to the fifth generation(5G)mobile communication system with limited network resources.At the same time,it further increases the complexity of resource optimization in 5G networks.The mobile communication system today is in its transition from the fourth generation(4G)to the 5G.More novel technologies and communication scenarios are emerging.Thus the traditional resource optimization methods hardly work with the new technologies and applications,meanwhile difficult to meet the needs of forthcoming massive and diverse user connections.Furthermore,the massive MIMO(Multiple-Input Multiple-Output)technology is adopted in 5G network deployment to improve system capacity and Spectral Efficiency(SE).However,the large antenna array needs a large number of radio frequency(RF)links to drive,it's very expensive and have a high energy consumption.This increases the system hardware design complexity,cost and energy consumption.In this paper,we optimize the system capacity,energy consumption and cost of massive MIMO network using efficient resource optimization methods.Using convex optimization theory,Lagrange dual method and multi-objective genetic algorithm,the joint antenna selection and user scheduling,the joint beam allocation and power optimization,and the joint resources allocation and power optimization for downlink transmission are researched,and a series of solutions and algorithms are derived.We realized the joint optimization of frequency resource,space resource and energy resource for massive MIMO system in 5G.The main contributions and innovative works of this paper are as follows:1.We studied the problem of joint antenna selection and user scheduling for massive MIMO system in 5G.In order to decrease the high hardware cost and high-power consumption caused by the large number of RF chains,we proposed a low-complexity joint antenna selection and user scheduling(JASUS)method based on Adaptive Markov Chain Monte Carlo(AMCMC)algorithm for multi-cell multi-user massive MIMO downlink systems.In our proposed method,only a small subset of base station(BS)transmit antennas is selected to serve predetermined active users,which reduces the number of RF chains.This method avoids heavy hardware costs and reduces power consumption caused by the selection of unnecessary transmit antennas to provide the required services.Performance analysis and simulation results show that the proposed algorithm achieve close performance to ES-based JASUS algorithm.At the same time,the computational complexity is reduced significantly by AMCMC.In our simulation experiments,when the SNR is 20 dB,the achieved values of cell capacity using ES and AMCMC algorithms are 50.7 bits/s/Hz and 49.9 bits/s/Hz,respectively.Using the proposed algorithm,we realized the joint optimization of space resource and energy resource for 5G multiuser massive MIMO system,while decreasing the hardware cost and power consumption and achieving maximum system capacity.2.We studied the problem of joint beam allocation and power optimization for massive MIMO sytem in 5G.In order to decrease the inter-beam interference and increase the quality of service for users,meanwhile to solve the problem of energy waste caused by the traditional beam allocation scheme with equal power allocation which cannot make full use of the system energy in massive MIMO sytem operating in millimeter wave(mmWave)band,we propose an efficient beam allocation and power optimization scheme.First,the problem of beam allocation and power optimization is formulated as a multivariate mixed-integer non-linear programming problem.Second,due to the non-convexity of the problem,we decompose it into two sub-problems which are beam allocation and power optimization.Finally,the beam allocation problem is solved using convex optimization technique.Realized the strategy that one beam can only serve one user,and also solved the problem of decreasing user service quality caused by inter-beam interference.In addition,we solve the power optimization problem in two steps.First,the non-convex problem is converted into a convex problem using a quadratic transformation.The second step implements Lagrange dual and sub-gradient methods to solve the optimization problem,meanwhile the problem of energy waste caused by the equal power allocation strategy can not full use of energy resources is solved.Performance analysis and simulation results show that the proposed algorithm performs almost identical to ES method.In our experiments,about 30.98 bits/J/Hz and 30.86 bits/J/Hz EE values were obtained by ES and proposed algorithms when the transmit power was 30 dBm.Furthermore,experiment results demonstrated that our proposed algorithm outperforms the greedy beam allocation method and the sub-optimal beam allocation methods in terms of average service ratio.Therefore,using the proposed algorithm,we realized the joint optimization of space resource and energy resource for 5G multiuser mmWave massive MIMO system,while increase system EE and average service ratio,and decreasing the hardware cost and power consumption of system.3.We studied the problem of joint resource allocation and power optimization for massive MIMO system in 5G.To solve the joint resource allocation and power optimization(JRAPO)problem in massive MIMO downlink system operating in mmWave band,this paper proposed a JRAPO scheme to maximize system energy efficiency(EE).At the first step,the problem of JRAPO is formulated as a multivariate mixed integer nonlinear programming problem.Then,because the non-convexity of this problem,we transformed it into a convex optimization problem by relaxing the resource block(RB)allocation indicator.Finally,Lagrangian dual decomposition algorithm and water-filling scheme are implemented to solve this joint optimization problem.We realized the optimization of system EE considering the different data rate requirements of different users.Simulation results demonstrated that our proposed algorithm outperforms both the RB allocation algorithm with equal power and the power allocation scheme with equal RBs in terms of system EE performance.Moreover,our proposed algorithm produced the highest user faireness in experiments.Therefore,using the proposed algorithm,we realized the joint optimization of frequency resource and energy resource for 5G multiuser mmWave massive MIMO system,while increase the SE and EE of system and better satisfaction of data rate requirements for users.