Study On Energy Efficiency Performance Of Massive MIMO Based On Swarm Intelligence Algorithm

Massive multiple input multiple output(MIMO)technology,which can greatly improve the capacity and spectral efficiency by allocating a large number of antennas at the transmitter,is one of the key technologies of the fifth generation(5G)communications.However,the deployment of so many antennas requires the same number of RF links,increasing the cost of the system while reducing energy efficiency.Therefore,it is urgent to improve the system energy efficiency.At present,there are also some problems in the energy efficiency optimization algorithms.Firstly,some of them ignore the power consumption of the circuit or regard it as a constant,and some of them do not consider the power consumption of the circuit hardware,which leads to the inaccuracy of the derived energy efficiency expression.Secondly,most of them only consider the impact of a factor on energy efficiency,ignoring the relationship between multiple parameters.Finally,traditional iterative optimization algorithm with two parameters is to fix one parameter first and then take the derivative of another parameter,which not only cannot optimize multiple parameters at the same time,but also has complex calculation.Aiming at the above problems,in order to improve the energy efficiency performance of the system under the premise of low complexity.In this thesis,the relationship between multiple parameters and their effects on energy efficiency are analyzed,and a joint optimization algorithm based on antenna selection and power allocation is proposed.On this basis,the particle swarm optimization(PSO)algorithm with revision strategy is introduced into the massive MIMO system,and the improved PSO energy efficiency optimization algorithms based on two-parameter and multi-parameter are proposed respectively.Finally,the proposed algorithms are applied to the scenarios of single-cell and multi-cell to analyze the performance.The specific contents are as follows:1.The effect of multiple parameters on the energy efficiency of massive MIMO system is analyzed,and a joint optimization algorithm based on antenna selection and power allocation is proposed.The results show that the parameters are mutually influenced.Moreover,in point-to-point massive MIMO downlink system,the energy efficiency of the combined antenna selection and power allocation algorithm is nearly doubled compared with that of the power allocation algorithm.2.Considering the relationship between the transmit antennas and the users,an improved two-parameter PSO energy efficiency optimization algorithm is proposed.The dynamic power consumption model is considered and the energy efficiency expressions of massive MIMO system under different scenarios are derived,which are regarded as the fitness functions of PSO algorithm.A revision strategy is proposed to jump out of the local optimal value under the premise of ensuring the efficiency and complexity of the algorithm.The simulation results show that,compared with the traditional iterative algorithm,the proposed algorithm requires fewer transmit antennas to achieve the optimal energy efficiency,and the running time is at most 1/12 of that of the iterative algorithm.3.On the basis of the improved two-parameter PSO energy efficiency optimization algorithm,a power consumption model including the circuit hardware is established.At the same time,considering the relationship among the transmit antennas,transmit power and users,an improved multi-parameter PSO energy efficiency optimization algorithm is proposed.Considering the hardware power of the circuit,a more realistic power consumption model is established.The transmit antennas,transmit power and users are regarded as particles with speed and position.The position and speed of the particles are constantly updated,and revision strategy is proposed to avoid falling into local optimum.Simulation results show that,compared with the improved two-parameter PSO energy efficiency optimization algorithm and traditional iterative algorithm,the proposed algorithm requires fewer transmit antennas to achieve the optimal energy efficiency in the single-cell scenarios.In multi-cell scenarios,the running time of the proposed algorithm is only 1/5 of that of the two-parameter PSO algorithm,and 1/62 of the traditional iterative algorithm.