Large-scale MIMO system precoding method applied in gathered user scene

Abstract

The invention discloses a large-scale MIMO system precoding method applied in a gathered user scene. The method comprises the following steps that 1, a base station acquires arrival angles of all terminal signals according to channel state information of user terminals; 2, the base station conducts clustering on the user terminals according to the arrival angles; 3, outer-layer precoding matrixes W of all user terminal clusters are designed according to the channel state information of the clusters; 4, equivalent channel state information matrixes are acquired according to the channel state information of all the clusters by combining the outer-layer precoding matrixes; 5, the base station designs inner-layer precoding matrixes Q1 according to the equivalent CSI matrixes on the basis of ZF-GMD-THP; and 6, precoding matrixes F (F=WQ) are obtained according to the outer-layer precoding matrixes W and the inner-layer precoding matrixes Q, and the base station sends data to a user through the precoding matrixes F. Compared with the prior art, the two-stage precoding method is adopted, therefore, the computing complexity can be effectively reduced, and the good performance can be achieved.

Description

technical field [0001] The invention belongs to the field of mobile communication technology and multi-antenna technology, and relates to an anti-interference method for intra-cell co-frequency interference, in particular to a massive MIMO system precoding method applied to a scenario of gathering users. Background technique [0002] At present, with the rapid development of wireless communication technology and mobile Internet, people continue to put forward higher requirements for mobile communication speed. However, system resources such as available spectrum and transmit power in wireless communication systems are limited, which cannot meet the increasing rate requirements. The massive MIMO (Multiple-Input Multiple-Output) system configures a large number of antennas (dozens or even hundreds) in the base station, and the number of base station antennas is far greater than the number of users served by the base station at the same time, so that the channel between the bas...

AI Technical Summary

Problems solved by technology

At this time, considering that the surrounding environment of the terminals gathered together is similar, and the relative positions of the antenna arrays are close, the downlink channel vectors from the base station to these terminals (assuming that the terminals are configured with a single antenna) have a large correlation. According to the precoding criterion, the downlink channel matrix composed of the base station and these terminals is quasi-ill-conditioned, that is, among the non-zero eigenvalues ​​of the autocorrelation matrix of the channel matrix, the smallest eigenvalue will be much smaller than the largest eigenvalue, resulting in zero-forcing Precoding will greatly amplify the transmitted signal power and degrade performance

Moreover, in a massive MIMO system, due to the large number of users, the complexity of the precoding algorithm based on zero-forcing is also a problem that needs to be considered

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