A Beamforming Method for NLOS Scenarios in Large-Scale MIMO

Abstract

The invention belongs to the field of communication technology, and discloses a beamforming method for NLOS scenarios in massive MIMO. A channel estimation matrix is ​​obtained at the base station side; a spatial correlation matrix is ​​obtained by performing autocorrelation on the channel estimation matrix; Ratio criterion construction optimization problem; equivalent minimization optimization problem; solving the optimal weight vector; normalization of the maximum value; Kronecker product with vertical antenna weights; The present invention has the advantages of low sensitivity to channel time-varying characteristics, strong anti-non-line-of-sight NLOS ability, low algorithm complexity, and simple implementation. The present invention finally improves the receiving power of desired users well. The invention solves the problem that the beamforming method in the prior art has weak ability to resist channel time variation, cannot effectively improve the receiving power of the desired user under multipath conditions, and has high algorithm complexity.

Description

technical field [0001] The invention belongs to the technical field of communications, and in particular relates to a beamforming method for NLOS scenarios in massive MIMO. Background technique [0002] At present, the existing technologies commonly used in the industry are as follows: [0003] After decades of rapid development, the popularity of small mobile communication devices, the performance changes related to the Internet of Things, and the demand for data services such as multimedia have shown explosive growth, and the traffic of mobile wireless communication has increased significantly. Facing such needs, long-term evolution ( LTE) and LTE-A will not be able to meet in the near future, so the fifth generation (5G) mobile communication system is expected to be realized in 2020, through technological evolution and innovation, 5G can fully utilize a series of emerging technologies to meet In the future, a wide range of data services and connections will be developed...

AI Technical Summary

Problems solved by technology

[0006] (1) In the prior art, for NLOS non-line-of-sight scenarios, since the energy of each path is too dispersed, the weight value obtained by the EBB algorithm cannot be guaranteed to be optimal, so the receiving power of the desired user cannot be effectively improved;

[0007] (2) The existing technology cannot realize the beamforming of the single-user massive MIMO system in the non-line-of-sight NLOS scenario, and cannot improve the receiving power of the desired user

[0008] (3) Existing technologies are more suitable for line-of-sight LOS scenarios, and there are relatively few beamforming studies for NLOS non-line-of-sight scenarios;

[0009] (4) The ZF algorithm and MRT algorithm in the prior art need to know the channel information of each path in real time, but in the multi-path channel scenario, the channel state information of each path cannot be fully known

[0010] (5) The ZF algorithm and MRT algorithm in the prior art are highly sensitive to the time-varying wireless channel, which may affect the performance of the system in actual system applications

[0012] The difficulty in solving the above problems lies in the fact that the existing technology is relatively sensitive to the time-varying nature of the wireless channel. If the time-varying nature of the wireless channel is not considered, it will affect the performance of the system in the actual system.

In addition, due to the existence of multiple scatterers in the non-line-of-sight NLOS scene, there will be multiple paths to the receiver. On the other hand, due to path loss, the power of each path to the receiver will be relatively low. Existing The weights obtained by the beamforming algorithm in the NLOS scenario are not necessarily optimal, and there is no way to effectively increase the received power of the desired user when the power of each path is low

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