Research On The Antenna Selection Algorithm In Massive MIMO Systems

With the development of the fifth generation of mobile communication technology(5G),mobile data users' demands for network stability,signal quality and transmission rate are increasing.In order to cope with their massive device connections,explosive growth in data volume,and emerging communication services,Low latency precision requirements and rich application scenarios,Massive MIMO(large-scale antenna technology)is the key technology to improve system capacity and spectrum utilization rate in the fifth generation mobile communication(5G),which has aroused widespread attention in the field of communication technology research.Research boom.Compared with the previous generation of MIMO technology,Massive MIMO has more significant performance advantages.It needs to deploy dozens or hundreds of antennas on the base station(BS)side to improve spectrum efficiency and channel capacity without increasing system bandwidth and power.But massive MIMO technology will increase the hardware complexity,economic cost and energy consumption,so the antenna selection technology to solve the above shortcomings arises at the historic moment.It can choose the best performance antenna from many antennas and reduce the hardware cost of the entire system.To improve transmission efficiency.First,this article introduces the basic architecture of Massive MIMO technology,as well as the current research status,background and classification of antenna selection algorithms,gives the basic model of antenna selection algorithms,and simulates the existing classic antenna selection algorithms and performs performance Compare and analyze some deficiencies in specific application scenarios.Then,the Massive MIMO antenna selection algorithm is studied in depth.In view of the shortcomings of existing algorithms,an improved dual-target research idea is proposed,which uses both channel capacity and bit error rate as performance indicators,and selects channel capacity and bit error rate with low performance loss to have better performance The antenna is more suitable forpractical application scenarios where both performances are fancy.The basic structure of the new dual objective function and improved Massive MIMO double-layer iterative antenna selection algorithm is established.The step-by-step process of antenna selection technology and signal detection is changed to be synchronized to eliminate interference between sub-channels that existed before antenna selection.The improved algorithm is more suitable for scenarios with low signal-to-noise,making the overall dual performance better.Next,the MIMO related channel matrix is studied to estimate the channel for unknown CSI(channel state information).Secondly,the lattice-based CLLL algorithm in MIMO detection is introduced into the antenna selection incremental algorithm,which makes the channel estimation matrix more orthogonal and the mutual interference between the sub-channels is smaller.A VLD algorithm that quickly detects the bit error rate is used.The detection of bit error rate reduces the computational complexity of the improved dual-target iterative algorithm.Finally,the performance of the improved Massive MIMO antenna selection algorithm is simulated and studied to verify its performance.First,compare the existing antenna selection algorithms,and select the suboptimal increasing algorithm to have the best performance in terms of synthesis,which is used as the basis for improving the algorithm.Then the accuracy of the channel estimation matrix and the applicability of the lattice reduction algorithm to the channel estimation matrix are verified.The simulation results show that the error is small.Then,the performance of the improved dual-target algorithm is verified.By changing the size of the parameters such as the number of antennas,the correlation coefficient between the antennas,and the size of the signal-to-noise ratio,the improved dual-target algorithm has better performance.Finally,the complexity of the algorithm is compared and analyzed.