Research Of Precoding In Massive MIMO System

Massive MIMO technology is one of the emerging wireless communication technologies, which can significantly improve the energy efficiency and spectral efficiency of communication systems. Therefore,it is widely expected by academia and industry and becomes the core of 5G mobile network Technology.With the improvement of the performance of the base station and the performance of the terminal, precoding technology, as an important branch of the large-scale MIMO technology, prepares the transmitted signal by using the channel state information to reduce the inter-cell interface and make the communication system obtain higher performance.Meanwhile, it is important that the linear precoding performance in massive MIMO system can approach to the non-linear precoding technology which is near-optimal performance. Therefore Massive MIMO linear precoding technology has become a 5G communication technology research hotspot.Based on this background, this paper studies the classical MIMO system transmission model, communication performance calculation and classical precoding technology. The main contents of this paper are as follows:First, this paper uses the traditional MIMO communication scene as the starting point to illustrate the system model of Massive MIMO and its advantages through mathematical formula, and explain the significance of linear precoding by introducing channel hardening. On the basis of this,this paper introduces and deduces the transmit rate the bit error rate of Zero-Force precoding, minimum mean square error precoding, singular value decomposition precoding and block diagonalization precoding.Secondly, aiming at the classical communication scenario of large-scale MIMO downlink communication system, firstly, we introduce and deduce the principle of Energy Penalty to prove the reason about transceiver redundancy at both sides. Then, considering the shortcomings of the regular zero-forcing algorithm and its improved algorithm, this paper proposes Cholesky Decomposition and Linear Preecoding of Sherman - Morrison Theorem. The algorithm takes advantage of the mathematical characteristics of the positive definite Hermite matrix and the advantage of the hardware pipeline processing matrix, and uses the iterative idea of the Sherman-Morrison theorem and the mathematical properties of the sparse vector matrix to obtain the exact result of the inverse matrix by finite iteration. The algorithm proposed in this paper not only guarantees the linear precoding algorithm with optimal performance of the algorithm, but also guarantees the lower computational complexity. The proposed algorithm has been greatly improved in the three communication indexes of rate, bit error rate and robustness.