| Summary: | 碩士 === 國立交通大學 === 電子研究所 === 105 === As the advancement of technology and the semiconductor process technology evolution, the hardware can be smaller and smaller. Mobile phone has become main device which the most people connect to the Internet in the outdoors. However, the requirement of the mobile communication substantial increases. The next generation 5G is more than ten times faster than 4G. It can produce the huge data rates because of the massive MIMO that is one of the most important technology identified by many people in 5G. The massive MIMO is equipped large number of the antennas at base station than mobile station several antennas. The base station antennas may be one hundred or one hundred and twenty-eight or bigger. Large scale antennas at base station can greatly raise data rates and have more efficiency exploiting spatial domain at time and frequency resources, but the channel estimation complexity is proportional to transmission antennas. Nevertheless, we can adopt TDD technique which has reciprocal property to only estimate uplink or downlink channel informations. Traditionally, estimating the MIMO and massive MIMO channel uses Eigenvalue Decomposition, Singular Value Decomposition, and linear signal processing methods such as MF and zero forcing, but EVD and SVD are very complexity in practice. To reduce complexity, we utilize subspace tracking algorithm. It exploits the approximation characteristic to find eigenvector improving performance and updates the last subspace per iteration to decrease complexity. Besides, using a few pilots improves phase ambiguity of the eigenvectors. Finally, we discuss zero forcing and four different methods to propose some suggestions.
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