Pilot Allocation In Massive MIMO System

Massive Multiple-Input Multiple-Output(MIMO)has attracted the attention of the industry due to its advantages of fast transmission rate,high spectrum utilization,and low power consumption.However,there are still some unsolved technical problems in this technology.One of the major bottlenecks is the pilot contamination problem.Pilot contamination is caused by multiplexing the same set of orthogonal pilot sequences between cells.The direct impact is that it will interfere with the uplink channel estimation,resulting in inaccurate channel estimation results,thus affecting system performance.Pilot contamination is inevitable,but its impact on system performance varies with pilot allocation strategy.Therefore,reasonable pilot allocation is one of the key research contents in massive MIMO systems.In this paper,the research focuses on optimizing pilot allocation to reduce pilot contamination,and proposes a pilot allocation scheme based on user location and user classification and two schemes based on improved genetic algorithm to search pilot multiplexing index matrix.This paper mainly works as follows:The first chapter is the introduction,expounds the development process of massive MIMO,and introduces the research status of pilot contamination problem and pilot allocation,and explains the innovative content and chapter arrangement of the article.The second chapter firstly compares the advantages of massive MIMO compared with traditional MIMO,and introduces its system model and performance analysis indicators in detail,analyzes and deduces the causes of pilot contamination and its impact on communication quality.Finally,several existing pilot allocation methods based on different ideas are introduced.In the third chapter,a massive MIMO multi-cell multi-user cellular communication system is proposed,which proposes a pilot allocation method for user classification.The method aims at improving the channel estimation performance,and designs the objective function and the threshold according to the distance from the terminal to the base station and the angle of arrival of the signal,and then compares the objective function with the threshold to classify the terminals in each cell in the system into high interference terminals and low.There are two types of interference terminals.Finally,pilot sequences are multiplexed for low-interference terminals,and additional orthogonal pilot sequences are allocated to high-interference terminals to mitigate pilot contamination.The simulation results show that the proposed method can improve the overall performance of the system while ensuring that there are no terminals with very poor performance in the cell,which ensures the fairness of users.In the fourth chapter,by analyzing the characteristics of pilot allocation,two allocation methods based on improved genetic algorithm to search the optimal pilot multiplexing index matrix to optimize system performance are proposed.The first method formulates the fitness function based on the distance between the users and the signal arrival angle,and applies the individual selection,chromosome crossing,and gene mutation to iteratively find the optimal solution in the largest evolutionary algebra.The simulation results show that the proposed algorithm has certain advantages in overall system performance and complexity,but there are still users with poor performance in the communication system.Therefore,the second method considers the fairness of users,and proposes to use the genetic algorithm to find the global optimal solution to achieve the best performance of the lowest user.The simulation results show that this scheme effectively improves the performance of the worst users with fewer iterations.The fifth chapter summarizes and forecasts,summarizes the shortcomings of the paper and proposes the future research.