Low Complexity DOA Estimation Algorithm For Massive MIMO Systems

The rapid development of the wireless communication technology and the sustained growth of the requirement of the communication data traffic have accelerated the arrival of the fifth generation mobile communication system.Because of the high utilization ratio of the spectrum and the robustness,the ma ssive MIMO system has attracted lots of attention,and it has been viewed as one of the most promising candidate technology for next generation wireless communication system.Meanwhile,the technology of Beamforming is an effective method to reduce interference and improve the performance of massive MIMO systems,and the performance of this technology is highly relies on information of the direction of users.So the direction of users estimation methods,or the direction of arrivals(DOA)estimation methods,are significant to massive MIMO systems.There exist many conventional DOA estimation algorithms,and the MUSIC method and the ESPRIT method has been viewed as the most effective DOA estimation methods,due to their high resolution.But in the massive MI MO systems,the high computational complexity brought by the large number of antenna elements will make them hardly suitable for massive MIMO systems.Then,the DOA estimation algorithm named Propagator Method(PM)has been proved efficient in terms of com putational complexity,because it does not require the eigenvalue decomposition of the covariance matrix of the received signals.In massive MIMO systems,however,the dimensions of the matrices involved in computation are increased due to the large number of antenna elements,so the computational complexity is waiting to be reduced further.In this thesis,low complexity DOA estimation algorithms for massive MIMO systems are studied.Some improved methods of PM algorithm with low computational complexity a re proposed,and simulation experiments are used to test and verify their performance.The main contributions of the thesis are summarized as follow.Firstly,in this thesis,the basic knowledge of array signal processing is presented.Based on some conventional antenna array models like uniform linear array,the principles of conventional DOA estimation algorithms such as MUSIC and ESPRIT are introduced.For massive MIMO systems,the principle of PM algorithm for planar rectangular array is introduced,and the computational complexity is analyzed.Focusing on the problem that the estimated parameters may be not pair matched in two dimensions DOA estimation issue,a pair matching method for PM algorithm is proposed to eliminate ambiguity error when the number of sources is bigger than one.Furthermore,in massive MIMO systems,the large number of antenna elements makes the dimensions of the matrices involved in computation enormous,which will bring lots of computational complexity and affect the system performance.To solve this problem,LLQ-PM algorithm is proposed.Via the symmetrical partition of the covariance matrix of received signals,the dimensions of the matrices involved in computation can be decreased.Meanwhile,the linear relationship contained in the array directional matrix can be reserved.So,the DOA estimation can be achieved,and the computational complexity is reduced.Moreover,in two dimensions DOA estimation issue,the PM algorithm requires the reconstruction of the system model,which will lead to high computational complexity.To solve this problem,2P-PM is proposed.Taking the advantages of non-symmetrical partition of the covariance matrix of received signals,the dimensions of the matrices involved in computation can be decreased.Moreover,the reconstruction of system model is avoided.So the computational complexity can be reduced significantly.