Novel channel estimation method of multi-user 3D MIMO system

Abstract

The invention discloses a novel channel estimation method for a multi-user 3D MIMO system, and the method comprises the steps: carrying out the modeling through employing an included angle between a direction of arrival and an x axis and an included angle between the direction of arrival and a y axis of a planar array antenna, and projecting a 3D MIMO channel to the x axis and the y axis; in the uplink preamble stage, allocating an orthogonal pilot frequency sequence to each user, and obtaining paired spatial features and optimal rotation angles of channels projected to the x axis and the y axis by each user; grouping the obtained paired spatial features, distributing the same pilot frequency information in the groups, and distributing orthogonal pilot frequency sequences among the groups;carrying out channel estimation through the spatial feature and the optimal rotation angle of the (n-1)th coherence time in the group of the nth coherence time after the leader stage, and then dynamically updating the spatial feature and the optimal rotation angle of the user; and repeating the intra-group channel estimation method to obtain spatial features and optimal rotation angles of all users in the cell, reconstructing channels of an x axis and a y axis of all users in the cell, and generating a 3D MIMO channel through a Kronecker product.

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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