Research On DOA Estimation Algorithms For Massive MIMO Systems

Multiple-input multiple-output(MIMO)technology is one of the key technologies to improve the spectrum efficiency and link reliability of wireless communication systems and has been studied intensively and extensively.A new technology called massive MIMO is focused widely by researchers because of its unique advantages in recent years.For example,the effects of small scale fading and thermal noise are eliminated and beamforming can be used to focus the signal on a particular point in the space accurately and to identify each user more precisely,which improve the system performance greatly.To implement beamforming,the accurate direction of arrival(DOA)estimation is a prerequisite.Due to different transmission conditions,the signal sources can be divided into point sources,multipath signals and distributed sources.In this thesis,the DOA estimation of these three kind of sources is studied in depth.Firstly,this thesis studies the DOA estimation algorithm of point sources in the massive MIMO systems.Based on the classical subspace-based algorithms and rotational invariance technique,a low-complexity 2-D DOA estimation algorithm is proposed in this thesis.Results show that the proposed algorithm provides a comparable estimation performance and a much lower computational cost.Secondly,this thesis studies the DOA estimation algorithm of multipath signals in the massive MIMO systems.The model of the multipath signals and the basic principles and steps of our algorithm are introduced then.Simulation results show that the proposed algorithm can provide better performance than the traditional algorithms.Finally,this thesis studies the DOA estimation algorithm of incoherently distributed sources.Based on the classical algorithms and beamspace transformation technology,the data vector is compressed,which results in lower computational complexity.Besides,the rotational invariance in the beamspace is derived and 2-D DOA estimation is performed by it.Results show that the proposed algorithm provides a comparable estimation performance and a lower computational cost.