Channel Acquisition And Transmission Scheme Optimization In Massive MIMO Wireless System

Massive Multiple Input Multiple Output (MIMO) technology can bring higher spectral efficiency and power efficiency, thus being one of the directions in the fifth generation with highest potential. Channel acqui-sition is important to the performance of beam form ing、user scheduling and transmission scheme. Besides, beamforming and scheduling algorithms commonly contain high-dimension matrix computation, thus, chan-nel acquisition and transmission scheme optimization in massive MIMO wireless system with low-complexity is meaningful. The main work of this thesis is as follows:Firstly, related classic algorithms in massive MIMO wireless system are summarized, in which zero forcing (ZF) precoding algorithm, minimum mean square error (MMSE) precoding algorithm, maximal ratio transmission (MRT) precoding algorithm, greedy user scheduling algorithm, iterative power projection (1PP) scheduling algorithm, K-means clustering algorithm, Improved K-means clustering algorithm, DFT based grouping algorithm, matrix transformation method (MTM) and frequency calibration (FC) method are re-viewed. MATLAB tool is used to simulate the performance of these algorithms. According to the simulation results, MMSE precoding performs best of the three precoding algorithms in sum rate, while the performance of ZF precoding can match that of MMSE precoding with relatively large signal to noise ratio (SNR). The greedy scheduling algorithm can get higher sum rate than IPP algorithm with much more complexity. Among user grouping algorithms, Improved K-means algorithm has the best performance while DFT based grouping method is free from massive matrix computation with high dimension. FC algorithm applies only to system with relatively small antenna number and includes matrix inversion computation while MTM algorithm has a much lower complexity and is more effective in massive MIMO system.Secondly, PASTd algorithm is applied to track the eigenspace of channel covariance, in this way, sta-tistical channel information can be tracked with much lower complexity than traditional method. Besides, Improved PASTd algorithm is proposed to improve the performance of PASTd algorithm. In FDD mode, the uplink eigenspace is different from the downlink eigenspace because of frequency offset, so phrase compensa-tion method (PCM) with low complexity is presented here to transform the uplink eigenspace to the downlink eigenspace with low complexity. MATLAB tool is used to simulate the performance of these algorithms, the simulation results show that PASTd algorithm has good converging quality and its performance can match that of batch processing method. PCM has little performance gap compared to MTM, but PCM has a lower complexity and performs better than MTM when the prior eigenspace is imperfect, thus being more suitable to massive MIMO system.Thirdly, based on statistical channel tracking, three user scheduling algorithms and two precoding algo-rithms are presented here. Particularly, Iterative Comparison of Eigenspace Correlation (ICEC) algorithm is proposed with the aim of choosing users with near orthogonal statistical channel in a greedy way and Iterative Comparison of Angle of Departure (AoD) correlation (ICAC) algorithm is presented to decrease the com-putation of ICEC algorithm with a little performance loss, besides, Grouping based Same-Order Scheduling (GSOS) algorithm, which clusters users with similar eigenspace into one group and then orders them accord-ing to the mean AoD and schedules users with the same order across groups, is presented. AS for the statistical channel based precoding method design, a statistical beamforming algorithm is presented by optimizing the lower bound of statistical ratio of signal to leakage and noise (SLNR), besides, a simplified beamforming algorithm with the most dominant eigenvector as the beamforming vector is proposed. At last, a complete system transmission scheme is presented based on eigenspace tracking. MATLAB tool is used to simulate the performance of these algorithms, according to the simulation results, ICEC algorithm is the best in sum rate among the three proposed user scheduling algorithms and ICAC algorithm has the minimum computation cost while GSOS algorithm provides a better tradeoff between sum rate and user fairness. Statistical beamforming method outperforms simplified beamforming algorithm in sum rate, but when combined with user scheduling, the performance of the two beamforming methods is almost the same. As for the simulation of the proposed transmission scheme, the results show that the performance of the proposed statistical transmission scheme can match that of traditional scheme based on instantaneous channel with estimate error to some extent.At last, instantaneous channel tracking and transmission scheme design are discussed in high speed railway (HSR) massive MIMO wireless system, Based on the location information of HSR, Kalman filter is used to track instantaneous channel with temporal and spatial correlation, besides, an algorithm to calculate optimal pilot beam pattern is presented to improve the tracking performance. In common, HSR has multiple terminals, so a user grouping method with users of quasiorthogonal channel in the same group is proposed to save the symbol resource with comparatively a little performance loss. Based on location information and beam training, two transmission schemes are presented. MATLAB tool is used to analyze the performance of these algorithms. The simulation results show that the proposed channel tracking scheme can track and predict channel well and the optimal beam pattern can provide better performance than random beam pattern. Besides, the proposed user grouping method can save about 50 percent of symbol resources and transmission scheme based on beam training and location information outperforms the scheme without beam training, which illustrates the necessity of beam training and the superiority of utilizing location information.