Research On Massive MIMO Wireless Transmission Technology Based On Channel Statistics

With the rapid development of mobile internet technology, the demand for data transmission of mobile users is increasing. In order to improve the rate of mobile communication and meet the growing demand of users, new generation of wireless transmission technology has been widely studied. MIMO transmission technology can make full use of the spatial resources, improve the capacity of wireless communication system and spectral efficiency, which has been applied to 4G. Based on traditional MIMO, massive MIMO deploy a large antenna array in base station, and make sure that the number of antennas is much larger than the number of users of serviced at the same time, which can serve more users on the same time frequency resources, and achieve higher data transmission rate and reliability. Meanwhile, the power effeciciency of massive MIMO is higher, and the interference is smaller. Massive MIMO is one of the key technologies of the next gener-ation mobile communication system. In this thesis, we mainly study the downlink transmission technology of massive MIMO multi-user system, and focus on investigating the massive MIMO wireless transmission technology using channel statistics.Firstly, we study the Joint Spatial Division and Multiplexing technology in massive MIMO system. We compare two user similarity metrics and several user grouping algorithms. We analyze the local optimal solution problem of user clustering algorithm and propose two improved user clustering algorithms. By opti-mizing the initial center of clustering algorithm, the final user group is more reasonable, and thus the overall performance of the system is improved. Simulation results show that the proposed scheme can obtain bet-ter user grouping performance and improve the system throughput performance, compared with traditional clustering algorithm.Secondly, we study channel estimation in FDD massive MIMO downlink system. Based on Joint Spatial Division and Multiplexing scheme, by utilizing the channel beams-block sparsity, we propose a beam-blocked compressive channel estimation method for FDD Massive MIMO Systems. This method can reduce pilot and uplink feedback overhead and improve the accuracy of channel state information recovery. Simulation results show that the proposed scheme has better performance than the traditional channel estimation method.Finally, we study the downlink multi-user transmission scheme in FDD massive MIMO heterogeneous networks and propose a space division interference control method based on cooperative beam form ing. The scheme utilizes the long time statistical channel state information to group all users of the system, and elimi-nate the interference between user groups and reduce the interference from macro station to small cell users by block diagonalization. Simulation results show that compared with the non cooperative precoding, the proposed coordinated beamforming space division interference control method can reduce the interference from macro station to small cell users, and improve the throughput performance of the system.