Multi-user millimeter wave large-scale MIMO channel estimation method

Abstract

The invention relates to a multi-user millimeter wave large-scale MIMO channel estimation method. The method comprises the following steps of 1) establishing a multi-user millimeter wave large-scale MIMO system and a channel model, 2) modeling signals received by multiple users into a third-order tensor model, and then acquiring a user group through convolution segmentation and K-means clustering, 3) establishing a user group dictionary containing multi-user channel information by using sparsity recovery of the signals, 4) performing dimensionality reduction on a user group channel by using manifold learning, and establishing a multi-user channel tensor dictionary manifold learning model, and 5) eliminating interference of other user group channels by using a tensor alternating direction method, and obtaining angle information and delay information of the user group channels by adopting multi-signal classification. Compared with the prior art, the method has the advantages that the problems of low resolution and inaccurate estimation in multi-user channel estimation can be solved, and the like.

Description

technical field [0001] The invention relates to the field of channel estimation in massive MIMO, in particular to a multi-user millimeter wave massive MIMO channel estimation method. Background technique [0002] In 5G communication, communication between multiple users needs to ensure strict quality of service, and multi-user millimeter wave massive MIMO technology has become one of the hot spots in 5G wireless communication systems. The transmitter and receiver of the multi-user millimeter wave massive MIMO technology configure a large number of antenna elements in a small space to obtain a large multiplexing gain and improve the capacity of wireless communication. As the number of antennas increases and the density of users increases, accurate and effective channel estimation for multi-user millimeter-wave massive MIMO systems has become a research hotspot in recent years. [0003] Many researches on channel estimation in multi-user millimeter-wave massive MIMO systems h...

AI Technical Summary

Problems solved by technology

However, since each user is estimated separately, in a multi-user mmWave massive MIMO system, its complexity increases with the number of users

The multiple signal classification method and the rotation invariance method, in the presence of Gaussian white noise, use the maximum likelihood estimator to obtain the best performance, but due to the need for multi-dimensional search, the calculation is very heavy, and the complexity and accuracy are not achieved degree of balance, and ignores the connection between users of the multi-user mmWave massive MIMO system

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