A Novel Channel Estimation Method for Multi-user 3d MIMO Systems

Abstract

The invention discloses a novel channel estimation method for a multi-user 3D MIMO system, which includes: modeling the angle between the direction of arrival and the x-axis and y-axis of a planar array antenna, and projecting the 3D MIMO channel to the x-axis and y-axis above the axis; the uplink preamble stage assigns an orthogonal pilot sequence to each user, and obtains the paired spatial characteristics and optimal rotation angles of the channels projected on the x-axis and y-axis by each user; for the obtained paired The spatial features are grouped, the same pilot information is allocated in the group, and the orthogonal pilot sequence is allocated between the groups; the spatial feature and optimal rotation of the n-1th coherent time are passed in the group of the n-th coherent time after the leading stage channel estimation, and then dynamically update the user's spatial characteristics and optimal rotation angle; repeat the intra-group channel estimation method to obtain the spatial characteristics and optimal rotation angle of all users in the cell, and reconstruct the x-axis and y-axis of all users in the cell The channels are generated by Kronecker products to generate 3D MIMO channels.

Description

technical field [0001] The invention belongs to the technical field of signal processing of 5G signals, and specifically relates to a novel channel estimation method for a multi-user 3D MIMO system. Background technique [0002] In recent years 5G (5 th -Generation) technology research is developing rapidly, among which Massive multiple-input multiple-output (Massive MIMO) has attracted great attention in both academia and industry, and 3D MIMO system as a Massive MIMO An application scenario can not only take advantage of the spatial freedom of large-scale transmit antennas, but also adjust the transmit beam direction in horizontal and vertical dimensions, which helps to improve the spatial resolution in the third dimension, thereby increasing signal power and reducing inter-cell interference. In order to obtain these potential advantages and benefits, it becomes particularly important to accurately obtain channel state information (channel state information, CSI) between ...

AI Technical Summary

Problems solved by technology

The low computational complexity of the LS method makes it widely used, but requires the use of orthogonal pilots and does not consider the impact of noise

The MMSE method considers the influence of noise on the channel and is an improvement on the LS method, but it needs to obtain prior knowledge such as the correlation matrix of the channel and the received noise power. However, the computational complexity increases with the increase of the number of antennas.

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