Research On Active Eavesdropping Security In Massive MIMO Systems

Massive Multiple Input Multiple Output technology is considered a breakthrough in wireless communication systems.It provides a base station with a large number of antennas and serves many users in the same time and frequency domain.The advantage of Massive M1M0 is the ability to concentrate the transmitted signal energy in a very narrow area,which will greatly improve system capacity.Avery important issue in Massive MIMO is secure communication.An important factor that affects secure communication is that eavesdroppers wiretap and attack the communication systems.Previous researchs show that passive eavesdropper has a negligible impact on Massive MIMO.So active eavesdropper becomes a key factor affecting secure communication.To solve this problem,this thesis studies the active eavesdropping security problem based on Massive MIMO.The work of this thesis mainly includes the following two aspects:Firstly,this thesis proposes an active eavesdropper detection scheme.The research scenario is single-cell single-user multi-eavesdropper MIMO system.By using additional random pilot and high-dimensional source counting algorithm,the active eavesdropper in Massive MIMO system is detected.Then this thesis discusses the superiority of the proposed scheme compared with the classic minimum description length(MDL)detector,and compares the two schemes.The results show that the detector in this thesis can obtain good performance without the pilot sequence of the transmitter much greater than the number of antennas of the base station.Secondly,the channel estimation under the active eavesdropping model is studied,and two kinds of channel estimation schemes are studied emphatically.Both schemes are studied under the signal model with random pilot.The first one is to use the open pilot as the training sequence to estimate the channel,the second is to use the minimum mean square error(MMSE)square error)signal detection techniques to determine the random pilot and then perform the channel estimation.After the simulation analysis,the normalized mean square error of the channel vector obtained in the latter is smaller than the former under the same condition,and has better estimation performance.In the end,this thesis combines the active eavesdropper detection scheme in Chapter 3,and analyzes the simulation results,and obtains the appropriate parameter setting of the power allocation factor and signal-to-noise ratio,which can achieve the better performance of active eavesdropper detection as well as the channel estimation performance.