Machine Learning Based Massive MIMO Physical Layer Authentication Algorithm

In order to meet the demands of the mobile communication system for speed,reliability and business contexts of the future,the fifth generation(5G)mobile communication network is developed.Massive Multiple Input Multiple Output(Massive MIMO)system acquires much higher system capacity than the traditional single antenna system,and is regarded as the one of the key techniques and foundations of 5G.The application of massive MIMO enriches the physical layer information resources of the system,providing a broad research space for the security technology based on physical layer information,and completely changing the system model at the same time.However,conventional physical layer authentication gives no consideration to the changes of channel models under massive MIMO system.Besides,it fails to make full use of the physical layer information and requires complicated computation.Therefore,how to utilize the abundant physical layer information of massive MIMO system to identify the users and avoid complex calculation has become a problem to be solved urgently.The main works is as follows:(1)The related contents of massive MIMO system and machine learning algorithm are studied.In massive MIMO model part,three common system models in geometry-based stochastic models(GBSMs)and four antenna array forms are studied,and the applicable scenarios of different models are analyzed.On machine learning algorithm,the principle and implementation of Support Vector Machine(SVM)algorithm,which is suitable for this scheme are studied,and the characteristics and application range of different kernel functions are summarized.These contents lay a foundation for further research.(2)This thesis employs the hypothesis testing method to express the specific problem of identity authentication,and proposes a channel authentication algorithm in massive MIMO system.The authentication algorithm employs the correlation-based stochastic models which contain the correlation channel model and the mutual coupling channel model,and the different antenna array forms.By combining arrival time,arrival angle and large scale fading factor of signals into feature vectors,a massive MIMO system authentication model based on Support Vector Machine(SVM)is established,and simulation platform is built.To achieve the goal of channel authentication in massive MIMO system,the channel feature vectors between different users should be extracted to distinguish them.The simulation results under different parameter configurations show that the proposed authentication algorithm achieve good performance in the physical layer authentication of massive MIMO system.(3)Furthermore,in order to simplify the process of optimization and improve the classification accuracy of SVM when the dimension of feature vectors is low,a SVM method based on Tabu search is proposed.Different from the traditional SVM solution of the two programming equations,the optimal hyperplane is constructed directly.The optimal hyperplane formula under the unconstrained premise is derived,and the Tabu search algorithm is used to obtain the global optimal hyperplane.The simulation results under different SNR show that the improved SVM authentication algorithm based on Tabu search is able to achieve higher classification accuracy than other classic SVM authentication algorithms based on kernel function.