Channel Estimation Of MASSIVE MIMO Using Compressive Sensing

As one of the key technologies of the 5G mobile communication system, MASSIVE MIMO has attracted concern and study. Equipped with more antennas in both side, the performance of the system is improved. However, more antennas brings difficulties in the estimation of the channel state information in the MASSIVE MIMO system. The performance of the classical algorithm based on pilot signal for the estimation of channel state information depends on the pilot signal. However, in practical scene channel coherence times are not long enough to allow for ideal pilot signal, and this question is more sever in the MASSIVE MIMO system. So, it is significant to develop algorithm for estimation channel state information which can reduce the influence of the pilot signal.In this thesis, we begin with researching the MASSIVE MIMO system, and pay attention to the technology of estimation of channel. Considering that COMPRESSIVE SENSING can reduce the length of sample and the requirement for the sample matrix for the sparse signal, in this thesis we research new algorithm which is based on COMPRESSIVE SENSING for channel estimation in the MASSIVE MIMO system.Firstly, we research the characteristics of technology and analyze the difficulties in channel estimation; Research kinds of classical algorithms such as least square which based on pilot and are used for float fading scene, and analysis their characteristics and their limits. For the scene of multi-user-multi-cell, we propose new algorithm for channel estimation of communication system which uses the MASSIVE MIMO technology. In this process, through reasonable modeling, we can simulate the large-scale fading and small-scale fading. Then we give two kinds of equivalent model which based on MASSIVE MIMO. Then, according to the compressive sensing we design new algorithm for channel estimation. The algorithm proposed in this thesis can reduce the strict requirement for training signal. At last, simulation in the MATLAB verify the performance of the algorithm in the direction of SNR and length of training signal. The results of the simulation show algorithm in this thesis can improve the performance of channel estimation in the MASSIVE MIMO system.