# An Improved Anti-Interference Precoding of Large-Scale Fading System Based on Channel Inversion
## 1 Introduction
Thus, the traffic between base stations stays constant even if the number of antennas tends to infinity. In [7], researchers derived a capacity lower bound for massive MIMO systems with LSFP and a finite number of base station antennas and considered optimal and suboptimal LSFP precodings that take into account all sources of interference. The simulations results showed that LSFP provides significant gain even for the case of moderate number of base station antennas [7]. However, the above anti-interference precoding has good performance only for a large number of antennas at a base station, reaching the value of hundreds to thousands, which increases the base station equipment costs and operating costs, and causes difficulties huge power consumption in practical applications. Namely, in order to improve the communication quality, hundreds of antennas need to be used, thus, the design complexity of the antenna array becomes too high. On the other hand, channel inversion represents a multi-user channel modulation technology. The vector signal generated between a user and a base station is used to form an inverse channel matrix so that the channels of each user reach the equilibrium during the transmission [8]. In [9], researchers compared the MMSE (minimum mean squared error) channel inversion algorithm and BMSN (Block Maximum Signal-to-Noise ratio) algorithm, which commonly utilizes the channel regularization with the pseudo-noise in the MU-MIMO transmission. The performance evaluation can be carried out through computer simulation. In [10], researchers applied the regularized channel inversion (RCI) combined with one of the 5G waveform candidates, namely generalized frequency division multiplexing (GFDM), in multiuser MIMO downlink systems and presents how the RCI in the multiuser MIMO-GFDM systems performs such good results as the RCI implemented in the multiuser MIMO-OFDM (Orthogonal Frequency Division Multiplexing) systems based on bit error rate (BER) parameter without causing a significant increase in system complexity. The design idea of the precoding LSFP based on the zero-forcing algorithm is relatively simple, but it can only effectively eliminate interference such as pilot pollution when the number of base station antennas is very large. Optimizing the design of the precoding LSFP can ensure that interference is effectively eliminated. However, it needs to increase the power of the transmitting device to achieve efficient communication. Aiming at the problem that the number of base station antennas of existing LSFP precoding is too large, the construction cost is too high. Based on the zero-forcing precoding algorithm and the optimized precoding algorithm, this paper applies channel inversion to improve and optimize the precoding. In the case of fewer base station antennas, interference such as pilot pollution and noise is suppressed, and the channel transmission rate is not affected at the same time, thereby achieving the purpose of further increasing the system rate and increasing the SINR. In this work, channel inversion technology is used to improve the standard large scale fading precoding (LSFP), obtaining an improved anti-interference precoding, which can effectively eliminate pilot contamination, noise and interference using a small number of antennas. First, we applied the channel inversion technique to derive the theoretical formula for the zero-forcing large scale fading precoding algorithm to obtain its SINR expression. Under ideal circumstances, we analyzed and proved that the improved zero-forcing precoding can achieve the ideal anti-interference performance. The communication transmission rate performance was verified in the simulation. Secondly, the channel inversion technique was used to derive the theoretical formula of the optimized precoding algorithm. The SINR expression was obtained. The performance of the communication transmission rate was verified in the simulation.