Research On 5G-based High-accuracy Indoor Localization Technologies

With the rapid development of Internet technology,location-based services will become increasingly popular in the field of public applications and public services.Following the operation and development of global satellite navigation system based on Global Position System,BeiDou Navigation Satellite System,Global Navigation Satellite System for many years,the outdoor location has been solved systematically,and high-accuracy and widerange-continuous-coverage indoor localization technology is increasingly expected.With the evolution of 5th-Generation Mobile Communication(5G)standards and the industrial demand of integration of communication and navigation,the problem of how to solve indoor high-accuracy localization in the 5G system is widely concerned by the academic and industrial community.Based on the key technique of 5G,this paper researches the key problems of indoor localization in the indoor deployment environment of5 G system,which include the obtaining of channel state information(CSI)and the overall design of localization system and localization algorithm.Existing researches indicate that range-based localization method which uses CSI to estimate distance is a feasible method.5G will apply Massive Multiple Input Multiple Output(Massive MIMO)technology,but the increasing number of antennas will cause that conventional algorithm can not estimate channel due to the lack of reference signal.On the other hand,though the compressed-sensing-based estimation algorithm can compute channel information accurately when there is a small number of reference signal.However,the performance of this kind of algorithm is influenced by channel sparseness,and the increasing number of antennas will also enhance the algorithm operation time.It is necessary to research how to reduce the algorithm operation time.To solve the problems above,this paper proposes an optimized orthogonal-matching-pursuit-based channel estimation algorithm,which reduces the iterations of the algorithm and apply stricter termination condition for the iteration.Simulation results indicate that the proposed optimized algorithm can reduce the algorithm operation time effectively and remove the influence of channel sparseness on algorithm performance.Though ultra-densification ensures that a large number of base stations exist in indoor environment,weak through-wall propagation ability of millimeter wave will cause that the limited number of base stations which can be linked by users and the requirement of the number of anchor nodes in traditional localization process can not be met.Moreover,the non line-ofsight propagation of signal affects traditional indoor localization technologies.To solve these problems,this paper proposes a localization system scheme,which includes a channel classification method which applies machine learning method to classify the channels into more accurate four categories,and a localization algorithm which combines virtual anchor nodes and reflection paths according to the result of channel classification.Simulation results show that the proposed channel classification method can improve the accuracy of classification efficiently.In addition,the presented localization algorithm not only can enhance the localization accuracy but also can realize high-accuracy localization when the number of anchor nodes is insufficient.